Patent Publication Number: US-2022221860-A1

Title: Adaptive navigation based on user intervention

Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     This application claims the benefit of priority of U.S. Provisional Patent Application No. 62/114,091, filed on Feb. 10, 2015; U.S. Provisional Patent Application No. 62/164,055, filed on May 20, 2015; U.S. Provisional Patent Application No. 62/170,728, filed on Jun. 4, 2015; U.S. Provisional Patent Application No. 62/181,784, filed on Jun. 19, 2015; U.S. Provisional Patent Application No. 62/192,576, filed on Jul. 15, 2015; U.S. Provisional Patent Application No. 62/215,764, filed on Sep. 9, 2015; U.S. Provisional Patent Application No. 62/219,733, filed on Sep. 17, 2015; U.S. Provisional Patent Application No. 62/261,578, filed on Dec. 1, 2015; U.S. Provisional Patent Application No. 62/261,598, filed on Dec. 1, 2015; U.S. Provisional Patent Application No. 62/267,643, filed on Dec. 15, 2015; U.S. Provisional Patent Application No. 62/269,818, filed on Dec. 18, 2015; U.S. Provisional Patent Application No. 62/270,408, filed on Dec. 21, 2015; U.S. Provisional Patent Application No. 62/270,418, filed on Dec. 21, 2015; U.S. Provisional Patent Application No. 62/270,431, filed on Dec. 21, 2015; U.S. Provisional Patent Application No. 62/271,103, filed on Dec. 22, 2015; U.S. Provisional Patent Application No. 62/274,883, filed on Jan. 5, 2016; U.S. Provisional Patent Application No. 62/274,968, filed on Jan. 5, 2016; U.S. Provisional Patent Application No. 62/275,007, filed on Jan. 5, 2016; U.S. Provisional Patent Application No. 62/275,046, filed on Jan. 5, 2016; and U.S. Provisional Patent Application No. 62/277,068, filed on Jan. 11, 2016. All of the foregoing applications are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure relates generally to autonomous vehicle navigation and a sparse map for autonomous vehicle navigation. Additionally, this disclosure relates to systems and methods for constructing, using, and updating the sparse map for autonomous vehicle navigation. 
     Background Information 
     As technology continues to advance, the goal of a fully autonomous vehicle that is capable of navigating on roadways is on the horizon. Autonomous vehicles may need to take into account a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination. For example, an autonomous vehicle may need to process and interpret visual information (e.g., information captured from a camera) and may also use information obtained from other sources (e.g., from a GPS device, a speed sensor, an accelerometer, a suspension sensor, etc.). At the same time, in order to navigate to a destination, an autonomous vehicle may also need to identify its location within a particular roadway (e.g., a specific lane within a multi-lane road), navigate alongside other vehicles, avoid obstacles and pedestrians, observe traffic signals and signs, and travel from on road to another road at appropriate intersections or interchanges. Harnessing and interpreting vast volumes of information collected by an autonomous vehicle as it travels to its destination poses a multitude of design challenges. The sheer quantity of data (e.g., captured image data, map data, GPS data, sensor data, etc.) that an autonomous vehicle may need to analyze, access, and/or store poses challenges that can in fact limit or even adversely affect autonomous navigation. Furthermore, if an autonomous vehicle relies on traditional mapping technology to navigate, the sheer volume of data needed to store and update the map poses daunting challenges. 
     SUMMARY 
     Embodiments consistent with the present disclosure provide systems and methods for autonomous vehicle navigation. The disclosed embodiments may use cameras to provide autonomous vehicle navigation features. For example, consistent with the disclosed embodiments, the disclosed systems may include one, two, or more cameras that monitor the environment of a vehicle. The disclosed systems may provide a navigational response based on, for example, an analysis of images captured by one or more of the cameras. The navigational response may also take into account other data including, for example, global positioning system (GPS) data, sensor data (e.g., from an accelerometer, a speed sensor, a suspension sensor, etc.), and/or other map data. 
     In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigation without requiring excessive data storage. 
     In other embodiments, the disclosed systems and methods may construct a road model for autonomous vehicle navigation. For example, the disclosed systems and methods may use crowd sourced data for autonomous vehicle navigation including recommended trajectories. As other examples, the disclosed systems and methods may identify landmarks in an environment of a vehicle and refine landmark positions. 
     In yet other embodiments, the disclosed systems and methods may use a sparse road model for autonomous vehicle navigation. For example, the disclosed systems and methods may provide navigation based on recognized landmarks, align a vehicle&#39;s tail for navigation, allow a vehicle to navigate road junctions, allow a vehicle to navigate using local overlapping maps, allow a vehicle to navigate using a sparse map, navigate based on an expected landmark location, autonomously navigate a road based on road signatures, provide forward navigation based on a rearward facing camera, navigate based on a free space determination, navigate in snow, provide autonomous vehicle speed calibration, determine lane assignment based on a recognized landmark location, and use super landmarks as navigation aids. 
     In still yet other embodiments, the disclosed systems and methods may provide adaptive autonomous navigation. For example, disclosed systems and methods may provide adaptive navigation based on user intervention, provide self-aware adaptive navigation, provide an adaptive road model manager, and manage a road model based on selective feedback. 
     In some embodiments, a non-transitory computer-readable medium may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks may be spaced apart by at least 50 meters, and wherein the sparse map may have a data density of no more than 1 megabyte per kilometer. 
     In some embodiments of the non-transitory computer-readable medium, the polynomial representation may be a three-dimensional polynomial representation. The polynomial representation of the target trajectory may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along the road segment. The plurality of predetermined landmarks may include a traffic sign represented in the sparse map by no more than 50 bytes of data. The plurality of predetermined landmarks may include a directional sign represented in the sparse map by no more than 50 bytes of data. The plurality of predetermined landmarks may include a general purpose sign represented in the sparse map by no more than 100 bytes of data. The plurality of predetermined landmarks may include a generally rectangular object represented in the sparse map by no more than 100 bytes of data. The representation of the generally rectangular object in the sparse map may include a condensed image signature associated with the generally rectangular object. The plurality of predetermined landmarks may be represented in the sparse map by parameters including landmark size, distance to previous landmark, landmark type, and landmark position. The plurality of predetermined landmarks included in the sparse map may be spaced apart by at least 2 kilometers. The plurality of predetermined landmarks included in the sparse map may be spaced apart by at least 1 kilometer. The plurality of predetermined landmarks included in the sparse map may be spaced apart by at least 100 meters. The sparse map may have a data density of no more than 100 kilobytes per kilometer. The sparse map may have a data density of no more than 10 kilobytes per kilometer. The plurality of predetermined landmarks may appear in the sparse map at a rate that is above a rate sufficient to maintain a longitudinal position determination accuracy within 1 meter. 
     In some embodiments, an autonomous vehicle may include a body; and a non-transitory computer-readable medium that may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer. The autonomous vehicle may include a processor configured to execute data included in the sparse map for providing autonomous vehicle navigation along the road segment. 
     In some embodiments of the autonomous vehicle, the polynomial representation may be a three-dimensional polynomial representation. The polynomial representation of the target trajectory may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along the road segment. 
     In some embodiments, an autonomous vehicle may include a body; and a processor configured to receive data included in a sparse map and execute the data for autonomous vehicle navigation along a road segment. The sparse map may include a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer. 
     In some embodiments, a method of processing vehicle navigation information for use in autonomous vehicle navigation may include receiving, by a server, navigation information from a plurality of vehicles. The navigation information from the plurality of vehicles may be associated with a common road segment. The method may include storing, by the server, the navigation information associated with the common road segment. The method may include generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles; and distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment. 
     In some embodiments of the method, the navigation information may include a trajectory from each of the plurality of vehicles as each vehicle travels over the common road segment. The trajectory may be determined based on sensed motion of a camera, including three-dimensional translation and three-dimensional rotational motions. The navigation information may include a lane assignment. Generating at least a portion of the autonomous vehicle road navigation model may include clustering vehicle trajectories along the common road segment and determining a target trajectory along the common road segment based on the clustered vehicle trajectories. The autonomous vehicle road navigation model may include a three-dimensional spline corresponding to the target trajectory along the common road segment. The target trajectory may be associated with a single lane of the common road segment. The autonomous vehicle road navigation model may include a plurality of target trajectories, each associated with a separate lane of the common road segment. Determining the target trajectory along the common road segment based on the clustered vehicle trajectories may include finding a mean or average trajectory based on the clustered vehicle trajectories. The target trajectory may be represented by a three-dimensional spline. The spline may be defined by less than 10 kilobytes per kilometer. The autonomous vehicle road navigation model may include identification of at least one landmark, including a position of the at least one landmark. The position of the at least one landmark may be determined based on position measurements performed using sensor systems associated with the plurality of vehicles. The position measurements may be averaged to obtain the position of the at least one landmark. The at least one landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, or a lamppost. 
     In some embodiments, a navigation system for a vehicle may include at least one processor programmed to receive from a camera, at least one environmental image associated with the vehicle; analyze the at least one environmental image to determine navigation information related to the vehicle; transmit the navigation information from the vehicle to a server. The at least one processor may be programmed to receive, from the server, an autonomous vehicle road navigation model. The autonomous vehicle road navigation model may include at least one update based on the transmitted navigation information. The at least one processor may be programmed to cause at least one navigational maneuver by the vehicle based on the autonomous vehicle road navigation model. 
     In some embodiments of the navigation system, the navigation information may include a trajectory from each of the plurality of vehicles as each vehicle travels over the common road segment. 
     In some embodiments, a server for processing vehicle navigation information for use in autonomous vehicle navigation may include a communication unit configured to communicate with a plurality of vehicles; and at least one processor programmed to receive, via the communication unit, the navigation information from the vehicles. The at least one processor may be programmed to generate at least a portion of an autonomous vehicle road navigation model based on the navigation information; and transmit at least the portion of the autonomous vehicle road navigation model to at least one of the vehicles to cause a navigational maneuver by the at least one of the vehicles based on the portion of the autonomous vehicle road navigation model. 
     In some embodiments of the server, the navigation information may include a trajectory from each of the plurality of vehicles as each vehicle travels over the common road segment. The portion of autonomous vehicle road navigation model may include an update to the autonomous vehicle road navigation model. 
     In some embodiments, a navigation system for a vehicle may include at least one processor programmed to receive, from one or more sensors, outputs indicative of a motion of the vehicle; determine an actual trajectory of the vehicle based on the outputs from the one or more sensors; receive, from a camera, at least one environmental image associated with the vehicle; analyze the at least one environmental image to determine information associated with at least one navigational constraint; determine a target trajectory, including the actual trajectory of the vehicle and one or more modifications to the actual trajectory based on the determined information associated with the at least one navigational constraint; and transmit the target trajectory from the vehicle to a server. 
     In some embodiments of the system, the one or more sensors may include a speed sensor. The one or more sensors may include an accelerometer. The one or more sensors may include the camera. The at least one navigational constraint may include at least one of a barrier, an object, a lane marking, a sign, or another vehicle. The camera may be included in the vehicle. 
     In some embodiments, a method of uploading a target trajectory to a server may include receiving, from one or more sensors, outputs indicative of a motion of a vehicle; determining an actual trajectory of the vehicle based on the outputs from the one or more sensors; receiving, from a camera, at least one environmental image associated with the vehicle; analyzing the at least one environmental image to determine information associated with at least one navigational constraint; determining a target trajectory, including the actual trajectory of the vehicle and one or more modifications to the actual trajectory based on the determined information associated with the at least one navigational constraint; and transmitting the target trajectory from the vehicle to a server. 
     In some embodiments of the method, the one or more sensors may include a speed sensor. The one or more sensors may include an accelerometer. The one or more sensors may include the camera. The at least one navigational constraint may include at least one of a barrier, an object, a lane marking, a sign, or another vehicle. The camera may be included in the vehicle. 
     In some embodiments, a system for identifying a landmark for use in autonomous vehicle navigation may include at least one processor programmed to: receive at least one identifier associated with the landmark; associate the landmark with a corresponding road segment; update an autonomous vehicle road navigation model relative to the corresponding road segment to include the at least one identifier associated with the landmark; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. The at least one identifier may be determined based on acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle; analysis of the at least one image to identify the landmark in the environment of the host vehicle; and analysis of the at least one image to determine the at least one identifier associated with the landmark. 
     In some embodiments of the system, the at least one identifier may include a position of the landmark. The at least one identifier may include a shape of the landmark. The at least one identifier may include a size of the landmark. The at least one identifier may include a distance of the landmark relative to another landmark. The at least one identifier may be determined based on the landmark being identified as one of a plurality of landmark types. The landmark types may include a traffic sign. The landmark types may include a post. The landmark types may include a directional indicator. The landmark types may include a rectangular sign. The at least one identifier further may include a condensed signature representation. The condensed signature representation of the landmark may be determined based on mapping an image of the landmark to a sequence of numbers of a predetermined data size. The condensed signature representation may indicate an appearance of the landmark. The condensed signature representation may indicate at least one of a color pattern of an image of the landmark or a brightness pattern of the image. The landmark may include at least one of a directional sign, a traffic sign, a lamppost, a road marking, and a business sign. 
     In some embodiments, a method of identifying a landmark for use in autonomous vehicle navigation may include receiving at least one identifier associated with the landmark; associating the landmark with a corresponding road segment; updating an autonomous vehicle road navigation model relative to the corresponding road segment to include the at least one identifier associated with the landmark; and distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. 
     In some embodiments, the method may include determining the at least one identifier. Determining the at least one identifier may include acquiring, from a camera associated with a host vehicle, at least one image representative of an environment of the host vehicle; analyzing the at least one image to identify the landmark in the environment of the host vehicle; and analyzing the at least one image to determine the at least one identifier associated with the landmark. The at least one identifier may include a distance of the landmark relative to another landmark, and wherein determining the at least one identifier includes determining a distance of the landmark relative to another landmark. The at least one identifier may further include a condensed signature representation, and wherein determining the at least one identifier includes determining the condensed signature representation from the at least one image. 
     In some embodiments, a system for determining a location of a landmark for use in navigation of an autonomous vehicle may include at least one processor programmed to: receive a measured position of the landmark; and determine a refined position of the landmark based on the measured position of the landmark and at least one previously acquired position for the landmark. The measured position and the at least one previously acquired position may be determined based on acquisition, from a camera associated with a host vehicle, of at least one environmental image associated with the host vehicle, analysis of the at least one environmental image to identify the landmark in the environment of the host vehicle, reception of global positioning system (GPS) data representing a location of the host vehicle, analysis of the at least one environmental image to determine a relative position of the identified landmark with respect to the host vehicle, and determination of a globally localized position of the landmark based on at least the GPS data and the determined relative position. 
     In some embodiments of the system, the landmark may include at least one of a traffic sign, an arrow, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, or a lamppost. Analysis of the at least one image to determine the relative position of the identified landmark with respect to the vehicle may include calculating a distance based on a scale associated with the at least one image. Analyzing the at least one image to determine the relative position of the identified landmark with respect to the vehicle may include calculating a distance based on an optical flow associated with the at least one image. The GPS data may be received from a GPS device included in the host vehicle. The camera may be included in the host vehicle. Determining the refined position of the landmark may include averaging the measured position of the landmark with the at least one previously acquired position. 
     In some embodiments, a method for determining a location of a landmark for use in navigation of an autonomous vehicle may include receiving a measured position of the landmark; and determining a refined position of the landmark based on the measured position of the landmark and at least one previously acquired position for the landmark. The measured position and the at least one previously acquired position may be determined based on acquisition, from a camera associated with a host vehicle, of at least one environmental image associated with the host vehicle, analysis of the at least one environmental image to identify the landmark in the environment of the host vehicle, reception of global positioning system (GPS) data representing a location of the host vehicle, analysis of the at least one environmental image to determine a relative position of the identified landmark with respect to the host vehicle, and determination of a globally localized position of the landmark based on at least the GPS data and the determined relative position. 
     In some embodiments of the method, the landmark may include at least one of a traffic sign, an arrow, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, or a lamppost. Analysis of the at least one image to determine the relative position of the identified landmark with respect to the vehicle may include calculating a distance based on a scale associated with the at least one image. Analysis of the at least one image to determine the relative position of the identified landmark with respect to the vehicle may include calculating a distance based on an optical flow associated with the at least one image. The GPS data may be received from a GPS device included in the host vehicle. The camera may be included in the host vehicle. Determining the refined position of the landmark may include averaging the measured position of the landmark with the at least one previously acquired position. 
     In some embodiments, an autonomous vehicle may include a body and at least one processor programmed to receive a measured position of the landmark; and determine a refined position of the landmark based on the measured position of the landmark and at least one previously acquired position for the landmark. The at least one processor may be further programmed to determine the measured position and the at least one previously acquired position based on acquisition, from a camera associated with the vehicle, of at least one environmental image associated with the vehicle, analysis of the at least one environmental image to identify the landmark in the environment of the vehicle, reception of global positioning system (GPS) data representing a location of the vehicle, analysis of the at least one environmental image to determine a relative position of the identified landmark with respect to the vehicle, and determination of a globally localized position of the landmark based on at least the GPS data and the determined relative position. 
     In some embodiments of the vehicle, the landmark may include at least one of a traffic sign, an arrow, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, or a lamppost. Analysis of the at least one image to determine the relative position of the identified landmark with respect to the vehicle may include calculating a distance based on a scale associated with the at least one image. Analyzing the at least one image to determine the relative position of the identified landmark with respect to the vehicle may include calculating a distance based on an optical flow associated with the at least one image. The GPS data may be received from a GPS device included in the host vehicle. Determining the refined position of the landmark may include averaging the measured position of the landmark with the at least one previously acquired position 
     In some embodiments, a system for autonomously navigating a vehicle along a road segment may include at least one processor programmed to: receive from an image capture device at least one image representative of an environment of the vehicle; analyze the at least one image to identify at least one recognized landmark; determine a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; and determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory. 
     In some embodiments of the system, the recognized landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The recognized landmark may include a change in spacing of lines on the road segment. The recognized landmark may include a sign for a business. The predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. Navigation between recognized landmarks may include integration of vehicle velocity to determine a location of the vehicle along the predetermined road model trajectory. The processor may be further programmed to adjust a steering system of the vehicle based on the autonomous steering action to navigate the vehicle. The processor may be further programmed to: determine a distance of the vehicle from the at least one recognized landmark; and determine whether the vehicle is positioned on the predetermined road model trajectory associated with the road segment based on the distance. The processor may be further programmed to adjust the steering system of the vehicle to move the vehicle from a current position of the vehicle to a position on the predetermined road model trajectory when the vehicle is not positioned on the predetermined road model trajectory. 
     In some embodiments, a vehicle may include a body; at least one image capture device configured to acquire at least one image representative of an environment of the vehicle; and at least one processor programmed to: receive from the at least one image capture device the at least one image; analyze the at least one image to identify at least one recognized landmark; determine a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; and determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory. 
     In some embodiments of the vehicle, the recognized landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, a lamppost, a change is spacing of lines on the road, or a sign for a business. The predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. Navigation between recognized landmarks may include integration of vehicle velocity to determine a location of the vehicle along the predetermined road model trajectory. The processor may be further programmed to adjust the steering system of the vehicle based on the autonomous steering action to navigate the vehicle. The processor may be further programmed to: determine a distance of the vehicle from the at least one recognized landmark; and determine whether the vehicle is positioned on the predetermined road model trajectory associated with the road segment based on the distance. The processor may be further programmed to adjust the steering system of the vehicle to move the vehicle from a current position of the vehicle to a position on the predetermined road model trajectory when the vehicle is not positioned on the predetermined road model trajectory. 
     In some embodiments, a method of navigating a vehicle may include receiving, from an image capture device associated with the vehicle, at least one image representative of an environment of the vehicle; analyzing, using a processor associated with the vehicle, the at least one image to identify at least one recognized landmark; determining a current position of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; determining an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory; and adjusting a steering system of the vehicle based on the autonomous steering action to navigate the vehicle. 
     In some embodiments, the method may include determining a location of the vehicle along the predetermined road model trajectory by integrating the vehicle velocity. The method may include determining, using the processor, a distance of the vehicle from the at least one recognized landmark; and determining whether the vehicle is positioned on the predetermined road model trajectory associated with the road segment based on the distance. The method may include determining a transformation required to move the vehicle from a current position of the vehicle to a position on the predetermined road model trajectory; and adjusting the steering system of the vehicle based on the transformation. 
     In some embodiments, a system for autonomously navigating an autonomous vehicle along a road segment may include at least one processor programmed to receive from an image capture device, a plurality of images representative of an environment of the autonomous vehicle; determine a traveled trajectory of the autonomous vehicle along the road segment based, at least in part, on analysis of one or more of the plurality of images; determine a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images; determine a heading direction for the autonomous vehicle based on the determined traveled trajectory; and determine a steering direction for the autonomous vehicle, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle. 
     In some embodiments of the system, the comparison between the traveled trajectory and the predetermined road model trajectory may include determination of a transformation that reduces an error between the traveled trajectory and the predetermined road model trajectory. The processor may be further programmed to adjust the steering system of the autonomous vehicle based on the transformation. The predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. The predetermined road model trajectory may be retrieved from a database stored in a memory included in the autonomous vehicle. The predetermined road model trajectory may be retrieved from a database accessible to the autonomous vehicle over a wireless communications interface. The image capture device may be included in the autonomous vehicle. Determination of the steering direction may be further based on one or more additional cues, including one or more of a left lane mark polynomial model, a right lane mark polynomial model, holistic path prediction, motion of a forward vehicle, determined free space ahead of the autonomous vehicle, and virtual lanes or virtual lane constraints determined based on positions of vehicles forward of the autonomous vehicle. Determination of the steering direction may be based on weights applied to the one or more additional cues. 
     In some embodiments, an autonomous vehicle may include a body; at least one image capture device configured to acquire at least one image representative of an environment of the autonomous vehicle; and at least one processor programmed to: receive from the image capture device, a plurality of images representative of the environment of the autonomous vehicle; determine a traveled trajectory of the autonomous vehicle along the road segment based, at least in part, on analysis of one or more of the plurality of images; determine a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images; determine a heading direction for the autonomous vehicle based on the determined traveled trajectory; and determine a steering direction for the autonomous vehicle, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle. 
     In some embodiments of the autonomous vehicle, the comparison between the traveled trajectory and the predetermined road model trajectory may include determination of a transformation that reduces an error between the traveled trajectory and the predetermined road model trajectory. The predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. The predetermined road model trajectory may be retrieved from one of a database stored in a memory included in the autonomous vehicle and a database accessible to the autonomous vehicle over a wireless communications interface. Determination of the steering direction may be further based on one or more additional cues, including one or more of a left lane mark polynomial model, a right lane mark polynomial model, holistic path prediction, motion of a forward vehicle, determined free space ahead of the autonomous vehicle, and virtual lanes or virtual lane constraints determined based on positions of vehicles forward of the autonomous vehicle. Determination of the steering direction may be based on weights applied to the one or more additional cues 
     In some embodiments, a method of navigating an autonomous vehicle may include receiving, from an image capture device, a plurality of images representative of an environment of the autonomous vehicle; determining a traveled trajectory of the autonomous vehicle along the road segment based, at least in part, on analysis of one or more of the plurality of images; determining a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images; determining a heading direction for the autonomous vehicle based on the determined traveled trajectory; and determining a steering direction for the autonomous vehicle, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle. 
     In some embodiments of the method, comparing the traveled trajectory to the predetermined road model trajectory may include determining a transformation that reduces an error between the traveled trajectory and the predetermined road model trajectory. Determining a steering direction may be based on one or more additional cues, including one or more of a left lane mark polynomial model, a right lane mark polynomial model, holistic path prediction, motion of a forward vehicle, determined free space ahead of the autonomous vehicle, and virtual lanes or virtual lane constraints determined based on positions of vehicles forward of the autonomous vehicle. Determining the steering direction may include applying weights to the one or more additional cues. 
     In some embodiments, a system for autonomously navigating a vehicle through a road junction may include at least one processor programmed to: receive from an image capture device at least one image representative of an environment of the vehicle; analyze the at least one image to identify two or more landmarks located in the environment of the vehicle; determine, for each of the two or more landmarks, a directional indicator relative to the vehicle; determine a current location of the vehicle relative to the road junction based on an intersection of the directional indicators for the two or more landmarks; determine a heading for the vehicle based on the directional indicators for the two or more landmarks; and determine a steering angle for the vehicle by comparing the vehicle heading with a predetermined road model trajectory at the current location of the vehicle. 
     In some embodiments of the system, the predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. The two or more landmarks may include three or more landmarks. The at least one processor may be further programmed to transmit a control signal specifying the steering angle to a steering system of the vehicle. The processor may be configured to retrieve the predetermined road model trajectory from a database stored in a memory included in the vehicle. The processor may be configured to retrieve the predetermined road model trajectory from a database accessible to the vehicle over a wireless communications interface. The camera may be included in the vehicle. The processor may be further programmed to determine the heading for the vehicle by: determining a previous location of the vehicle relative to the road junction based on the intersection of the directional indicators for the two or more landmarks; and determining the heading based on the previous location and the current location. 
     In some embodiments, an autonomous vehicle may include a body; at least one image capture device configured to acquire at least one image representative of an environment of the vehicle; and at least one processor programmed to: receive from a camera at least one image representative of an environment of the vehicle; analyze the at least one image to identify two or more landmarks located in the environment of the vehicle; determine, for each of the two or more landmarks, a directional indicator relative to the vehicle; determine a current location of the vehicle relative to the road junction based on an intersection of the directional indicators for the two or more landmarks; determine a heading for the vehicle based on the directional indicators for the two or more landmarks; and determine a steering angle for the vehicle by comparing the vehicle heading with a predetermined road model trajectory at the current location of the vehicle. 
     In some embodiments of the vehicle, the predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. The two or more landmarks may include three or more landmarks. The at least one processor may be further programmed to transmit a control signal specifying the steering angle to a steering system of the vehicle. The predetermined road model trajectory may be retrieved from one of a database stored in a memory included in the vehicle and a database accessible to the vehicle over a wireless communications interface. The processor may be further programmed to determine a heading for the vehicle by: determining a previous location of the vehicle relative to the road junction based on the intersection of the directional indicators for the two or more landmarks; and determining the heading based on the previous location and the current location. 
     In some embodiments, a method of navigating an autonomous vehicle may include receiving, from an image capture device, at least one image representative of an environment of the vehicle; analyzing, using at least one processor, the at least one image to identify two or more landmarks located in the environment of the vehicle; determining, for each of the two or more landmarks, a directional indicator relative to the vehicle; determining a current location of the vehicle relative to the road junction based on an intersection of the directional indicators for the two or more landmarks; determining a heading for the vehicle based on the directional indicators for the two or more landmarks; and determining a steering angle for the vehicle by comparing the vehicle heading with a predetermined road model trajectory at the current location of the vehicle. 
     In some embodiments of the method, the predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. The method may include retrieving the predetermined road model trajectory from one of a database stored in a memory included in the vehicle and a database accessible to the vehicle over a wireless communications interface. The method may include transmitting a control signal specifying the steering angle to a steering system of the vehicle. Determining the heading for the vehicle may include determining a previous location of the vehicle relative to the road junction based on the intersection of the directional indicators for the two or more landmarks; and determining the heading based on the previous location and the current location. 
     In some embodiments, a system for autonomously navigating a vehicle based on a plurality of overlapping navigational maps may include at least one processor programmed to: receive a first navigational map for use in autonomously controlling the vehicle, wherein the first navigational map is associated with a first road segment; determine at least a first autonomous navigational response for the vehicle along the first road segment based on analysis of the first navigational map; receive a second navigational map for use in autonomously controlling the vehicle, wherein the second navigational map is associated with a second road segment, wherein the first road segment is different from the second road segment, and wherein the first road segment and the second road segment overlap one another at an overlap segment; determine at least a second autonomous navigational response for the vehicle along the second road segment based on analysis of the second navigational map; and determine at least a third autonomous navigational response for the vehicle in the overlap segment based on at least one of the first navigational map and the second navigational map. 
     In some embodiments of the system, each of the plurality of overlapping navigational maps may have its own coordinate frame. Each of the plurality of overlapping navigational maps may include a polynomial representation of a target trajectory along a road segment. Each of the overlapping navigational maps may be a sparse map having a data density of no more than 10 kilobytes per kilometer. The overlap segment may have a length of at least 50 meters. The overlap segment may have a length of at least 100 meters. The at least one processor may be programmed to determine the third autonomous navigational response based on both the first navigational map and the second navigational map. The third autonomous navigational response may be a combination of the first autonomous navigational response and the second autonomous navigational response. The third autonomous navigational response may be an average of the first autonomous navigational response and the second autonomous navigational response. The processor may be further programmed to: determine an error between the first autonomous navigational response and the second autonomous navigational response; and determine the third autonomous navigational response based on the second autonomous navigational response when the error is less than a threshold error. 
     In some embodiments, an autonomous vehicle may include a body; at least one image capture device configured to acquire at least one image representative of an environment of the vehicle; at least one processor programmed to: determine a current location of the vehicle based on the at least one image; receive a first navigational map associated with a first road segment; determine at least a first autonomous navigational response for the vehicle based on analysis of the first navigational map, when the current location of the vehicle lies on the first navigational map; receive a second navigational map associated with a second road segment different from the second road segment, the first road segment and the second road segment overlapping one another at an overlap segment; determine at least a second autonomous navigational response for the vehicle based on analysis of the second navigational map when the current location of the vehicle lies on the second navigational map; and determine at least a third autonomous navigational response for the vehicle based on at least one of the first navigational map and the second navigational map when the current location of the vehicle lies in the overlap segment. 
     In some embodiments of the autonomous vehicle, each of the first navigational map and the second navigational map may have its own coordinate frame. Each of the first navigational map and the second navigational map may include a polynomial representation of a target trajectory along a road segment. The at least one processor may be programmed to determine the third autonomous navigational response based on both the first navigational map and the second navigational map. The third autonomous navigational response may be a combination of the first autonomous navigational response and the second autonomous navigational response. The processor may be further programmed to: determine an error between the first autonomous navigational response and the second autonomous navigational response; and determine the third autonomous navigational response based on the second autonomous navigational response when the error is less than a threshold error. 
     In some embodiments, a method of navigating an autonomous vehicle may include receiving from an image capture device, at least one image representative of an environment of the vehicle; determining, using a processor associated with the vehicle, a current location of the vehicle based on the at least one image; receiving a first navigational map associated with a first road segment; determining at least a first autonomous navigational response for the vehicle based on analysis of the first navigational map, when the current location of the vehicle lies on the first navigational map; receiving a second navigational map associated with a second road segment different from the second road segment, the first road segment and the second road segment overlapping one another at an overlap segment; determining at least a second autonomous navigational response for the vehicle based on analysis of the second navigational map when the current location of the vehicle lies on the second navigational map; and determining at least a third autonomous navigational response for the vehicle based on at least one of the first navigational map and the second navigational map when the current location of the vehicle lies in the overlap segment. 
     In some embodiments of the method, each of the plurality of overlapping navigational maps may have its own coordinate frame, and each of the plurality of overlapping navigational maps may include a polynomial representation of a target trajectory along a road segment. Determining the third autonomous navigational response may include determining a combination of the first autonomous navigational response and the second autonomous navigational response. The method may include determining an error between the first autonomous navigational response and the second autonomous navigational response; and determining the third autonomous navigational response based on the second autonomous navigational response when the error is less than a threshold error. 
     In some embodiments, a system for sparse map autonomous navigation of a vehicle along a road segment may include at least one processor programmed to: receive a sparse map of the road segment, wherein the sparse map has a data density of no more than  1  megabyte per kilometer; receive from a camera, at least one image representative of an environment of the vehicle; analyze the sparse map and the at least one image received from the camera; and determine an autonomous navigational response for the vehicle based solely on the analysis of the sparse map and the at least one image received from the camera. 
     In some embodiments of the system, the sparse map may include a polynomial representation of a target trajectory along the road segment. The sparse map may include one or more recognized landmarks. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 0.5 per kilometer. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 1 per kilometer. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 1 per 100 meters. The sparse map may have a data density of no more than 100 kilobytes per kilometer. The sparse map may have a data density of no more than 10 kilobytes per kilometer. 
     In some embodiments, a method for sparse map autonomous navigation of a vehicle along a road segment may include receiving a sparse map of the road segment, wherein the sparse map has a data density of no more than 1 megabyte per kilometer; receiving from a camera, at least one image representative of an environment of the vehicle; analyzing the sparse map and the at least one image received from the camera; and determining an autonomous navigational response for the vehicle based solely on the analysis of the sparse map and the at least one image received from the camera. 
     In some embodiments of the method, the sparse map may include a polynomial representation of a target trajectory along the road segment. The sparse map may include one or more recognized landmarks. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 0.5 per kilometer. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 1 per kilometer. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 1 per 100 meters. The sparse map may have a data density of no more than 100 kilobytes per kilometer. The sparse map may have a data density of no more than 10 kilobytes per kilometer. 
     In some embodiments, a non-transitory computer readable medium may store instructions causing at least one processor to perform sparse map autonomous navigation of a vehicle along a road segment, which may include receiving a sparse map of the road segment. The instructions may cause the processor to perform the steps of: receiving a sparse map of the road segment, wherein the sparse map has a data density of no more than 1 megabyte per kilometer; receiving from a camera, at least one image representative of an environment of the vehicle; analyzing the sparse map and the at least one image received from the camera; and determining an autonomous navigational response for the vehicle based solely on the analysis of the sparse map and the at least one image received from the camera. 
     In some embodiments of the non-transitory computer readable medium, the sparse map may include a polynomial representation of a target trajectory along the road segment. The sparse map may include one or more recognized landmarks. The recognized landmarks may be spaced apart in the sparse map at a rate of no more than 0.5 per kilometer. 
     In some embodiments, a system for autonomously navigating a vehicle along a road segment based on a predetermined landmark location may include at least one processor programmed to: receive from a camera, at least one image representative of an environment of the vehicle; determine a position of the vehicle along a predetermined road model trajectory associated with the road segment based, at least in part, on information associated with the at least one image; identify a recognized landmark forward of the vehicle based on the determined position, wherein the recognized landmark is beyond a sight range of the camera; determine a current distance between the vehicle and the recognized landmark by comparing the determined position of the vehicle with a predetermined position of the recognized landmark; and determine an autonomous navigational response for the vehicle based on the determined current distance. 
     In some embodiments of the system, the predetermined position of the recognized landmark may be determined as an average of a plurality of acquired position measurements associated with the recognized landmark, wherein the plurality of acquired position measurements are determined based on acquisition of at least one environmental image , analysis of the at least one environmental image to identify the recognized landmark in the environment, reception of global positioning system (GPS) data, analysis of the at least one environmental image to determine a relative position of the recognized landmark with respect to the vehicle, and determination of a globally localized position of the recognized landmark based on at least the GPS data and the determined relative position. The autonomous navigational response may include application of brakes associated with the vehicle. The autonomous navigational response may include modifying a steering angle of the vehicle. The recognized landmark may include a stop line, a traffic light, a stop sign, or a curve along the road segment. The camera may be included in the vehicle. 
     In some embodiments, a method for autonomously navigating a vehicle along a road segment based on a predetermined landmark location may include receiving from a camera, at least one image representative of an environment of the vehicle; determining a position of the vehicle along a predetermined road model trajectory associated with the road segment based, at least in part, on information associated with the at least one image; identifying a recognized landmark forward of the vehicle based on the determined position, wherein the recognized landmark is beyond a sight range of the camera; determining a current distance between the vehicle and the recognized landmark by comparing the determined position of the vehicle with a predetermined position of the recognized landmark; and determining an autonomous navigational response for the vehicle based on the determined current distance. 
     In some embodiments of the method, the predetermined position of the recognized landmark may be determined as an average of a plurality of acquired position measurements associated with the recognized landmark, wherein the plurality of acquired position measurements may be determined based on acquisition of at least one environmental image, analysis of the at least one environmental image to identify the recognized landmark in the environment, reception of global positioning system (GPS) data, analysis of the at least one environmental image to determine a relative position of the recognized landmark with respect to the vehicle, and determination of a globally localized position of the recognized landmark based on at least the GPS data and the determined relative position. The autonomous navigational response may include application of brakes associated with the vehicle. The autonomous navigational response may include modifying a steering angle of the vehicle. The recognized landmark may include a stop line, a traffic light, a stop sign, or a curve along the road segment. The camera may be included in the vehicle. 
     In some embodiments, a non-transitory computer readable medium may store instructions causing at least one processor to perform autonomous navigation of a vehicle along a road segment. The instructions may cause the processor to perform the steps of: receiving from a camera, at least one image representative of an environment of the vehicle; determining a position of the vehicle along a predetermined road model trajectory associated with the road segment based, at least in part, on information associated with the at least one image; identifying a recognized landmark forward of the vehicle based on the determined position, wherein the recognized landmark is beyond a sight range of the camera; determining a current distance between the vehicle and the recognized landmark by comparing the determined position of the vehicle with a predetermined position of the recognized landmark; and determining an autonomous navigational response for the vehicle based on the determined current distance. 
     In some embodiments of the non-transitory computer readable medium, the autonomous navigational response may include application of brakes associated with the vehicle. The autonomous navigational response may include modifying a steering angle of the vehicle. The recognized landmark may include a stop line, a traffic light, a stop sign, or a curve along the road segment. 
     In some embodiments, a system for autonomously navigating a vehicle along a road segment may include at least one processor programmed to: receive, from at least one sensor, information relating to one or more aspects of the road segment; determine a local feature of the road segment based on the received information; compare the local feature to a predetermined signature feature for the road segment; determine a current location of the vehicle along a predetermined road model trajectory associated with the road segment based on the comparison of the local feature and the predetermined signature feature; and determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined location. 
     In some embodiments of the system, the at least one processor may be further programmed to: determine a heading direction of the vehicle at the current location, and determine the autonomous steering action by comparing the direction of the predetermined road model trajectory with the heading direction. The heading direction may be determined based on a traveled trajectory of the vehicle. The at least one sensor may include an image capture device configured to acquire at least one image representative of an environment of the vehicle. The signature feature may include a road width profile over at least a portion of the road segment. The signature feature may include a lane width profile over at least a portion of the road segment. The signature feature may include a dashed line spacing profile over at least a portion of the road segment. The signature feature may include a predetermined number of road markings along at least a portion of the road segment. The signature feature may include a road surface profile over at least a portion of the road segment. The signature feature may include a predetermined curvature associated with the road segment. Determining the current location of the vehicle may include comparing first parameter values indicative of a curvature of the predetermined road model trajectory and second parameter values indicative of a curvature of a measured trajectory for the vehicle. The at least one sensor may include a suspension component monitor. 
     In some embodiments, a vehicle may include a body; at least one sensor configured to acquire information relating to one or more aspects of the road segment; and at least one processor programmed to: determine a local feature of the road segment based on the information received from the at least one sensor; compare the local feature to a predetermined signature feature for the road segment; determine a current location of the vehicle along a predetermined road model trajectory associated with the road segment based on the comparison of the local feature and the predetermined signature feature; and determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the current location. 
     In some embodiments of the vehicle, the signature feature may include at least one of a road width profile over at least a portion of the road segment, a lane width profile over at least a portion of the road segment, a dashed line spacing profile over at least a portion of the road segment, a predetermined number of road markings along at least a portion of the road segment, a road surface profile over at least a portion of the road segment, and a predetermined curvature associated with the road segment. The vehicle may include a suspension component monitor, wherein the processor is further programmed to determine the local feature based on signals from the suspension component monitor. The processor may be further programmed to: determine a heading direction of the vehicle; determine a direction of the predetermined road model trajectory at the current location; and determine the autonomous steering action by comparing the direction with the heading direction. 
     In some embodiments, a method of navigating a vehicle may include receiving, from at least one sensor, information relating to one or more aspects of the road segment; determining, using at least one processor, a local feature of the road segment based on the information received from the at least one sensor; comparing the received information to a predetermined signature feature for the road segment; determining a current location of the vehicle along a predetermined road model trajectory associated with the road segment based on the comparison of the received information and the predetermined signature feature; and determining an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the current location. 
     In some embodiments, the method may include determining a heading direction of the vehicle at the current location; determining the direction of the predetermined road model trajectory at the current location; and determining the autonomous steering action by comparing the direction of the predetermined road model trajectory with the heading direction. The local feature may include at least one of a road width profile over at least a portion of the road segment, a lane width profile over at least a portion of the road segment, a dashed line spacing profile over at least a portion of the road segment, a predetermined number of road markings along at least a portion of the road segment, a road surface profile over at least a portion of the road segment, and a predetermined curvature associated with the road segment. The method may include determining, using a suspension component monitor, a road surface profile; comparing the road surface profile with a predetermined road surface profile; and determining the current location based on the comparison of the road surface profile and the predetermined road surface profile. 
     In some embodiments, a system for autonomously navigating a vehicle may include at least one processor programmed to: receive from a rearward facing camera, at least one image representing an area at a rear of the vehicle; analyze the at least one rearward facing image to locate in the image a representation of at least one landmark; determine at least one indicator of position of the landmark relative to the vehicle; determine a forward trajectory for the vehicle based, at least in part, upon the indicator of position of the landmark relative to the vehicle; and cause the vehicle to navigate along the determined forward trajectory. 
     In some embodiments of the system, the indicator of position may include a distance between the vehicle and the landmark. The indicator of position may include a relative angle between the vehicle and the landmark. The landmark may include a road edge, a lane marking, a reflector, a pole, a change in line pattern on a road, or a road sign. The landmark may include a backside of a road sign. The at least one processor may be further programmed to determine a lane offset amount of the vehicle within a current lane of travel based on the indicator of position of the landmark, and wherein determination of the forward trajectory is further based on the determined lane offset amount. The at least one processor may be further programmed to receive from another camera, at least one image representing another area of the vehicle, and wherein the determination of the forward trajectory is further based on the at least one image received from the another camera. 
     In some embodiments, a method of autonomously navigating a vehicle may include receiving from a rearward facing camera, at least one image representing an area at a rear of the vehicle; 
     analyzing the at least one rearward facing image to locate in the image a representation of at least one landmark; determining at least one indicator of position of the landmark relative to the vehicle; determining a forward trajectory for the vehicle based, at least in part, upon the indicator of position of the landmark relative to the vehicle; and causing the vehicle to navigate along the determined forward trajectory. 
     In some embodiments of the method, the indicator of position may include a distance between the vehicle and the landmark. The indicator of position may include a relative angle between the vehicle and the landmark. The landmark may include a road edge, a lane marking, a reflector, a pole, a change in line pattern on a road, or a road sign. The landmark may include a backside of a road sign. The method may include determining a lane offset amount of the vehicle within a current lane of travel based on the indicator of position of the landmark, and wherein the determining of the forward trajectory may be based on the determined lane offset amount. 
     In some embodiments, a vehicle may include a body; a rearward facing camera; and at least one processor programmed to: receive, via a rearward camera interface connecting the rearward facing camera, at least one image representing an area at a rear of the vehicle; analyze the at least one rearward facing image to locate in the image a representation of at least one landmark; determine at least one indicator of position of the landmark relative to the vehicle; determine a forward trajectory for the vehicle based, at least in part, upon the indicator of position of the landmark relative to the vehicle; and 
     cause the vehicle to navigate along the determined forward trajectory. 
     In some embodiments of the vehicle, the rearward facing camera may be mounted on an object connected to the vehicle. The object may be a trailer, a bike carrier, a ski/snowboard carrier, a mounting base, or a luggage carrier. The rearward camera interface may include a detachable interface. The rearward camera interface may include a wireless interface. 
     In some embodiments, a system for navigating a vehicle by determining a free space region in which a vehicle can travel may include at least one processor programmed to: receive from an image capture device, a plurality of images associated with an environment of a vehicle; analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary; wherein the first free space boundary, the second free space boundary, and the forward free space boundary define a free space region forward of the vehicle; determine a navigational path for the vehicle through the free space region; and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle. 
     In some embodiments of the system, the first free space boundary may correspond to at least one of a road edge, a curb, a barrier, a lane dividing structure, a parked vehicle, a tunnel wall, or a bridge structure. The second free space boundary may correspond to at least one of a road edge, a curb, a barrier, a lane dividing structure, a parked vehicle, a tunnel wall, or a bridge structure. The forward free space boundary may correspond to a road horizon line. The at least one processor may be further programmed to identify, based on analysis of the at least one of the plurality of images, an obstacle forward of the vehicle and exclude the identified obstacle from the free space region forward of the vehicle. The obstacle may include a pedestrian. The obstacle may include another vehicle. The obstacle may include debris. The at least one processor may be further programmed to identify, based on analysis of the at least one of the plurality of images, an obstacle forward of the vehicle and exclude a region surrounding the identified obstacle from the free space region forward of the vehicle. The at least one processor may be further programmed to determine the region surrounding the identified obstacle based on one or more of the following: a speed of the vehicle, a type of the obstacle, an image capture rate of the image capture device, and a movement speed of the obstacle. 
     In some embodiments, a vehicle may include a body, the body including a driver side and a passenger side; an image capture device; and at least one processor programmed to: receive from the image capture device, a plurality of images associated with an environment of the vehicle; analyze at least one of the plurality of images to identify a first free space boundary on the driver side of the body and extending forward of the body, a second free space boundary on the passenger side of the body and extending forward of the body, and a forward free space boundary forward of the body and extending between the first free space boundary and the second free space boundary; wherein the first free space boundary, the second free space boundary, and the forward free space boundary define a free space region forward of the body; determine a navigational path for the vehicle through the free space region; and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle. 
     In some embodiments, a method of navigating a vehicle by determining a free space region in which a vehicle can travel may include receiving from an image capture device, a plurality of images associated with an environment of a vehicle; analyzing at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary; wherein the first free space boundary, the second free space boundary, and the forward free space boundary define a free space region forward of the vehicle; determining a navigational path for the vehicle through the free space region; and causing the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle. 
     In some embodiments of the method, the first free space boundary may correspond to at least one of a road edge, a curb, a barrier, a lane dividing structure, a parked vehicle, a tunnel wall, or a bridge structure. The second free space boundary may correspond to at least one of a road edge, a curb, a barrier, a lane dividing structure, a parked vehicle, a tunnel wall, or a bridge structure. The forward free space boundary may correspond to a road horizon line. The method may include identifying, based on analysis of the at least one of the plurality of images, an obstacle forward of the vehicle; and excluding the identified obstacle from the free space region forward of the vehicle. The obstacle may include a pedestrian. The obstacle may include another vehicle. The obstacle may include debris. The method may include identifying, based on analysis of the at least one of the plurality of images, an obstacle forward of the vehicle; and excluding a region surrounding the identified obstacle from the free space region forward of the vehicle. The method may include determining the region surrounding the identified obstacle based on one or more of the following: a speed of the vehicle, a type of the obstacle, an image capture rate of the image capture device, and a movement speed of the obstacle. 
     In some embodiments, a system for navigating a vehicle on a road with snow covering at least some lane markings and road edges may include at least one processor programmed to: receive from an image capture device, at least one environmental image forward of the vehicle, including areas where snow covers at least some lane markings and road edges; identify, based on an analysis of the at least one image, at least a portion of the road that is covered with snow and probable locations for road edges bounding the at least a portion of the road that is covered with snow; and cause the vehicle to navigate a navigational path that includes the identified portion of the road and falls within the determined probable locations for the road edges. 
     In some embodiments of the system, the analysis of the at least one image may include identifying at least one tire track in the snow. The analysis of the at least one image may include identifying a change of light across a surface of the snow. The analysis of the at least one image may include identifying a plurality of trees along an edge of the road. The analysis of the at least one image may include recognizing a change in curvature at a surface of the snow. The recognized change in curvature may be determined to correspond to a probable location of a road edge. The analysis of the at least one image may include a pixel analysis of the at least one image in which at least a first pixel is compared to at least a second pixel in order to determine a feature associated with a surface of the snow covering at least some lane markings and road edges. The feature may correspond to an edge of a tire track. The feature may correspond to an edge of the road. The at least one processor may be further programmed to cause the vehicle to navigate between determined edges of the road. The at least one processor may be further programmed to cause the vehicle to navigate by at least partially following tire tracks in the snow. 
     In some embodiments, a method of navigating a vehicle on a road with snow covering at least some lane markings and road edges may include receiving from an image capture device, at least one environmental image forward of the vehicle, including areas where snow covers at least some lane markings and road edges; identifying, based on an analysis of the at least one image, at least a portion of the road that is covered with snow and probable locations for road edges bounding the at least a portion of the road that is covered with snow; and causing the vehicle to navigate a navigational path that includes the identified portion of the road and falls within the determined probable locations for the road edges. 
     In some embodiments of the method, the analysis of the at least one image may include identifying at least one tire track in the snow. The analysis of the at least one image may include identifying a change of light across a surface of the snow. The analysis of the at least one image may include identifying a plurality of trees along an edge of the road. The analysis of the at least one image may include recognizing a change in curvature at a surface of the snow. The recognized change in curvature may be determined to correspond to a probable location of a road edge. The analysis of the at least one image may include a pixel analysis of the at least one image in which at least a first pixel is compared to at least a second pixel in order to determine a feature associated with a surface of the snow covering at least some lane markings and road edges. The feature may correspond to an edge of a tire track. The feature may correspond to an edge of the road. The method may include causing the vehicle to navigate between determined edges of the road. The method may include causing the vehicle to navigate by at least partially following tire tracks in the snow. 
     In some embodiments, a system for navigating a vehicle on a road at least partially covered with snow may include at least one processor programmed to: receive from an image capture device, a plurality of images captured of an environment forward of the vehicle, including areas where snow covers a road on which the vehicle travels; analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary; wherein the first free space boundary, the second free space boundary, and the forward free space boundary define a free space region forward of the vehicle; determine a first proposed navigational path for the vehicle through the free space region; provide the at least one of the plurality of images to a neural network and receive from the neural network a second proposed navigational path for the vehicle based on analysis of the at least one of the plurality of images by the neural network; determine whether the first proposed navigational path agrees with the second proposed navigational path; and cause the vehicle to travel on at least a portion of the first proposed navigational path if the first proposed navigational path is determined to agree with the second proposed navigational path. 
     In some embodiments, a system for calibrating an indicator of speed of an autonomous vehicle may include at least one processor programmed to: receive from a camera a plurality of images representative of an environment of the vehicle; analyze the plurality of images to identify at least two recognized landmarks; determine, based on known locations of the two recognized landmarks, a value indicative of a distance between the at least two recognized landmarks; determine, based on an output of at least one sensor associated with the autonomous vehicle, a measured distance between the at least two landmarks; and determine a correction factor for the at least one sensor based on a comparison of the value indicative of the distance between the at least to recognized landmarks and the measured distance between the at least two landmarks. 
     In some embodiments of the system, the correction factor may be determined such that an operation on the determined distance along the road segment by the correction factor matches the distance value received via the wireless transceiver. The two recognized landmarks may include one or more of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The at least one sensor may include a speedometer associated with the vehicle. The known locations of the two recognized landmarks may be received from a server based system located remotely with respect to the vehicle. Each of the known locations may constitute a refined location determined based on a plurality of GPS-based measurements. 
     In some embodiments, a system for calibrating an indicator of speed of an autonomous vehicle may include at least one processor programmed to: determine a distance along a road segment based on an output of at least one sensor associated with the autonomous vehicle; receive, via a wireless transceiver, a distance value associated with the road segment; and determine a correction factor for the at least one sensor based on the determined distance along the road segment and the distance value received via the wireless transceiver. 
     In some embodiments of the system, the distance value associated with the road segment, received via the wireless transceiver, may be determined based on prior measurements made by a plurality of measuring vehicles. The plurality of measuring vehicles may include at least 100 measuring vehicles. The plurality of measuring vehicles may include at least 1000 measuring vehicles. The correction factor may be determined such that an operation on the determined distance along the road segment by the correction factor matches the distance value received via the wireless transceiver. The at least one processor may be programmed to determine a composite correction factor based on a plurality of determined correction factors. The composite correction factor may be determined by averaging the plurality of determined correction factors. The composite correction factor may be determined by finding a mean of the plurality of determined correction factors. 
     In some embodiments, a vehicle may include a body; a camera; and at least one processor programmed to: receive from the camera a plurality of images representative of an environment of the vehicle; analyze the plurality of images to identify at least two recognized landmarks; determine, based on known locations of the two recognized landmarks, a value indicative of a distance between the at least two recognized landmarks; determine, based on an output of at least one sensor associated with the autonomous vehicle, a measured distance between the at least two landmarks; and determine a correction factor for the at least one sensor based on a comparison of the value indicative of the distance between the at least to recognized landmarks and the measured distance between the at least two landmarks. 
     In some embodiments of the vehicle, the at least one sensor may include a speedometer associated with the vehicle. The two recognized landmarks may include one or more of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The known locations of the two recognized landmarks may be received from a server based system located remotely with respect to the vehicle. 
     In some embodiments, a system for determining a lane assignment for an autonomous vehicle along a road segment may include at least one processor programmed to: receive from a camera at least one image representative of an environment of the vehicle; analyze the at least one image to identify at least one recognized landmark; determine an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark; and determine a lane assignment of the vehicle along the road segment based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark. 
     In some embodiments of the system, the environment of the vehicle may include the road segment, a number of lanes, and the at least one recognized landmark. The at least one recognized landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The at least one recognized landmark may include a sign for a business. The lateral offset distance between the vehicle and the at least one recognized landmark may be a sum of a first distance between the vehicle and a first side of the road segment and a second distance between the first side of the road and the at least one recognized landmark. The determination of the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark may be based on a predetermined position of the at least one recognized landmark. The determination of the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark may be based on a scale associated with the at least one image. The determination of the lane assignment may be further based on at least one of a width of the road segment, a number of lanes of the road segment, and a lane width. The determination of the lane assignment may be further based on a predetermined road model trajectory associated with the road segment. The at least one recognized landmark may include a first recognized landmark on a first side of the vehicle and a second recognized landmark on a second side of the vehicle and wherein determination of the lane assignment of the vehicle along the road segment is based on a first indicator of lateral offset distance between the vehicle and the first recognized landmark and a second indicator of lateral offset distance between the vehicle and the second recognized landmark. 
     In some embodiments, a computer-implemented method for determining a lane assignment for an autonomous vehicle along a road segment may include the following operations performed by one or more processors: receiving from a camera at least one image representative of an environment of the vehicle; analyzing the at least one image to identify at least one recognized landmark; determining an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark; and determining a lane assignment of the vehicle along the road segment based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark. 
     In some embodiments of the method, the at least one recognized landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The at least one recognized landmark may include a sign for a business. The determination of the lane assignment may be further based on a predetermined road model trajectory associated with the road segment. The at least one recognized landmark may include a first recognized landmark on a first side of the vehicle and a second recognized landmark on a second side of the vehicle and wherein determination of the lane assignment of the vehicle along the road segment is based on a first indicator of lateral offset distance between the vehicle and the first recognized landmark and a second indicator of lateral offset distance between the vehicle and the second recognized landmark. 
     In some embodiments, a computer-readable storage medium may include a set of instructions that are executable by at least one processor to cause the at least one processor to perform a method for determining a lane assignment for an autonomous vehicle along a road segment. The method may include receiving from a camera at least one image representative of an environment of the vehicle; analyzing the at least one image to identify at least one recognized landmark; determining an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark; and determining a lane assignment of the vehicle along the road segment based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark. 
     In some embodiments of the computer-readable storage medium, the at least one recognized landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The at least one recognized landmark may include a sign for a business. The determination of the lane assignment may be further based on a predetermined road model trajectory associated with the road segment. The at least one recognized landmark may include a first recognized landmark on a first side of the vehicle and a second recognized landmark on a second side of the vehicle and wherein determination of the lane assignment of the vehicle along the road segment is based on a first indicator of lateral offset distance between the vehicle and the first recognized landmark and a second indicator of lateral offset distance between the vehicle and the second recognized landmark. 
     In some embodiments, a system for autonomously navigating a vehicle along a road segment may include at least one processor programmed to: receive from a camera at least one image representative of an environment of the vehicle; analyze the at least one image to identify at least one recognized landmark, wherein the at least one recognized landmark is part of a group of recognized landmarks, and identification of the at least one recognized landmark is based, at least in part, upon one or more landmark group characteristics associated with the group of recognized landmarks; determine a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; and determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory. 
     In some embodiments of the system, the at least one recognized landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The at least one recognized landmark may include a sign for a business. The predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. The at least one processor may be further programmed to determine a current location of the vehicle along the predetermined road model trajectory based on a vehicle velocity. The one or more landmark group characteristics may include relative distances between members of the group of recognized landmarks. The one or more landmark group characteristics may include an ordering sequence of members of the group of recognized landmarks. The one or more landmark group characteristics may include a number of landmarks included in the group of recognized landmarks. Identification of the at least one recognized landmark may be based, at least in part, upon a super landmark signature associated with the group of recognized landmarks. The at least one processor may be programmed to determine an autonomous steering action for the vehicle by comparing a heading direction of the vehicle to the predetermined road model trajectory at the determined current location of the vehicle. 
     In some embodiments, a computer-implemented method for autonomously navigating a vehicle along a road segment may include the following operations performed by one or more processors: receiving from a camera at least one image representative of an environment of the vehicle; analyzing the at least one image to identify at least one recognized landmark, wherein the at least one recognized landmark is part of a group of recognized landmarks, and identification of the at least one recognized landmark is based, at least in part, upon one or more landmark group characteristics associated with the group of recognized landmarks; determining, relative to the vehicle, a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; and determining an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory. 
     In some embodiments of the method, the at least one recognized landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The at least one recognized landmark may include a sign for a business. The one or more landmark group characteristics may include relative distances between members of the group of recognized landmarks. The one or more landmark group characteristics may include an ordering sequence of members of the group of recognized landmarks. 
     In some embodiments, a computer-readable storage medium may include a set of instructions that are executable by at least one processor to cause the at least one processor to perform a method for autonomously navigating a vehicle along a road segment. The method may include receiving from a camera at least one image representative of an environment of the vehicle; analyzing the at least one image to identify at least one recognized landmark, wherein the at least one recognized landmark is part of a group of recognized landmarks, and identification of the at least one recognized landmark is based, at least in part, upon one or more landmark group characteristics associated with the group of recognized landmarks; determining, relative to the vehicle, a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; and determining an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory. 
     In some embodiments of the computer-readable storage medium, the at least one landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. The at least one recognized landmark may include a sign for a business. The one or more landmark group characteristics may include relative distances between members of the group of recognized landmarks. The one or more landmark group characteristics may include an ordering sequence of members of the group of recognized landmarks. 
     In some embodiments, a navigation system for a vehicle may include at least one processor programmed to: receive from a camera, at least one environmental image associated with the vehicle; determine a navigational maneuver for the vehicle based on analysis of the at least one environmental image; cause the vehicle to initiate the navigational maneuver; receive a user input, associated with a user&#39;s navigational response different from the initiated navigational maneuver; determine navigational situation information relating to the vehicle based on the received user input; and store the navigational situation information in association with information relating to the user input. 
     In some embodiments of the system, the navigational maneuver may be based on a recognized landmark identified in the at least one environmental image. The information relating to the user input may include information specifying at least one of a degree of a turn of the vehicle, an amount of an acceleration of the vehicle, and an amount of braking of the vehicle. The control system may include at least one of a steering control, an acceleration control, and a braking control. The navigational situation information may include one or more images captured by a camera onboard the vehicle. The user input may include at least one of braking, steering, or accelerating. The navigational situation information may include a location of the vehicle. The navigational situation information may include at least one output of a sensor onboard the vehicle. The sensor may be a speedometer. The sensor may be an accelerometer. The sensor may be an IR sensor. The navigational situation information may include a time of day. The navigational situation information may include an indication of the presence of a vision inhibitor. The vision inhibitor may be caused by glare. The navigational situation information may be determined based on the at least one environmental image. The system may include a transmitter for sending the navigational situation information to a server remote from the vehicle. 
     In some embodiments, a non-transitory computer-readable medium may include instructions that are executable by at least one processor to cause the at least one processor to perform a method. The method may include receiving from a camera, at least one environmental image associated with the vehicle; determining a navigational maneuver for the vehicle based on analysis of the at least one environmental image; causing the vehicle to initiate the navigational maneuver; receiving a user input, associated with a user&#39;s navigational response different from the initiated navigational maneuver; determining navigational situation information relating to the vehicle based on the received user input; and storing the navigational situation information in association with information relating to the user input. 
     In some embodiments, a navigation system for a vehicle may include at least one processor programmed to: determine a navigational maneuver for the vehicle based, at least in part, on a comparison of a motion of the vehicle with respect to a predetermined model representative of a road segment; receive from a camera, at least one image representative of an environment of the vehicle; determine, based on analysis of the at least one image, an existence in the environment of the vehicle of a navigational adjustment condition; cause the vehicle to adjust the navigational maneuver based on the existence of the navigational adjustment condition; and store information relating to the navigational adjustment condition. 
     In some embodiments of the system, the navigational adjustment condition may include a parked car. The navigational adjustment condition may include a lane shift. The navigational adjustment condition may include at least one of a newly encountered traffic sign or a newly encountered traffic light. The navigational adjustment condition may include an area of construction. The processor may be further programmed to cause the stored information relating to the navigational adjustment condition to be transmitted to a road model management system for determining whether an update to the predetermined model representative of the road segment is warranted by the navigational adjustment condition. The information stored relative to the navigational adjustment condition may include at least one of an indicator of location where the navigational adjustment condition was encountered, an indication of the adjustment made to the navigational maneuver, and the at least one image. The predetermined model representative of the road segment may include a three-dimensional spline representing a predetermined path of travel along the road segment. 
     In some embodiments, a method for navigating a vehicle may include determining a navigational maneuver for the vehicle based, at least in part, on a comparison of a motion of the vehicle with respect to a predetermined model representative of a road segment; receiving from a camera, at least one image representative of an environment of the vehicle; determining, based on analysis of the at least one image, an existence in the environment of the vehicle of a navigational adjustment condition; causing the vehicle to adjust the navigational maneuver based on the existence of the navigational adjustment condition; and storing information relating to the navigational adjustment condition. 
     In some embodiments of the method, the navigational adjustment condition may include a parked car. The navigational adjustment condition may include a lane shift. The navigational adjustment condition may include at least one of a newly encountered traffic sign or a newly encountered traffic light. The navigational adjustment condition may include an area of construction. The method may include causing the stored information relating to the navigational adjustment condition to be transmitted to a road model management system for determining whether an update to the predetermined model representative of the road segment is warranted by the navigational adjustment condition. The information stored relative to the navigational adjustment condition may include at least one of an indicator of location where the navigational adjustment condition was encountered, an indication of the adjustment made to the navigational maneuver, and the at least one image. The predetermined model representative of the road segment may include a three-dimensional spline representing a predetermined path of travel along the road segment. 
     In some embodiments, a non-transitory computer-readable medium may include instructions that are executable by at least one processor to cause the at least one processor to perform a method. The method may include determining a navigational maneuver for the vehicle based, at least in part, on a comparison of a motion of the vehicle with respect to a predetermined model representative of a road segment; receiving from a camera, at least one image representative of an environment of the vehicle; determining, based on analysis of the at least one image, an existence in the environment of the vehicle of a navigational adjustment condition; causing the vehicle to adjust the navigational maneuver based on the existence of the navigational adjustment condition; and storing information relating to the navigational adjustment condition. 
     In some embodiments of the computer-readable medium, the method may include causing the stored information relating to the navigational adjustment condition to be transmitted to a road model management system for determining whether an update to the predetermined model representative of the road segment is warranted by the navigational adjustment condition. The information stored relative to the navigational adjustment condition may include at least one of an indicator of location where the navigational adjustment condition was encountered, an indication of the adjustment made to the navigational maneuver, and the at least one image. The predetermined model representative of the road segment may include a three-dimensional spline representing a predetermined path of travel along the road segment. 
     In some embodiments, a system for interacting with a plurality of autonomous vehicles may include a memory including a predetermined model representative of at least one road segment; and at least one processor programmed to: receive from each of the plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver; analyze the navigational situation information; determine, based on the analysis of the navigational situation information, whether the adjustment to the determined navigational maneuver was due to a transient condition; and update the predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition. 
     In some embodiments of the system, the predetermined model representative of at least one road segment may include a three-dimensional spline representing a predetermined path of travel along the at least one road segment. The update to the predetermined model may include an update to the three-dimensional spline representing a predetermined path of travel along the at least one road segment. The adjustment to a determined navigational maneuver may be resulted from a user intervention. The adjustment to a determined navigational maneuver may be resulted from an automatic determination, based on image analysis, of an existence in a vehicle environment of a navigational adjustment condition. The navigational situation information may include at least one image representing an environment of an autonomous vehicle. The navigational situation information may include a video representing an environment of an autonomous vehicle. The transient condition may be associated with a parked car, an intervening car, a pedestrian, a low light condition, a glare condition, a temporary barrier, or temporary roadwork. 
     In some embodiments, a method for interacting with a plurality of autonomous vehicles may include receiving from each of the plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver; analyzing the navigational situation information; determining, based on the analysis of the navigational situation information, whether the adjustment to the determined navigational maneuver was due to a transient condition; and updating a predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition. 
     In some embodiments of the method, the predetermined model representative of at least one road segment may include a three-dimensional spline representing a predetermined path of travel along the at least one road segment. The update to the predetermined model may include an update to the three-dimensional spline representing a predetermined path of travel along the at least one road segment. The adjustment to a determined navigational maneuver may be resulted from a user intervention. The adjustment to a determined navigational maneuver may be resulted from an automatic determination, based on image analysis, of an existence in a vehicle environment of a navigational adjustment condition. The navigational situation information may include at least one image representing an environment of an autonomous vehicle. The navigational situation information may include a video representing an environment of an autonomous vehicle. The transient condition may be associated with a parked car, an intervening car, a pedestrian, a low light condition, a glare condition, a temporary barrier, or temporary roadwork. 
     In some embodiments, a non-transitory computer-readable medium may include instructions that are executable by at least one processor to cause the at least one processor to perform a method. The method may include receiving from each of the plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver; analyzing the navigational situation information; determining, based on the analysis of the navigational situation information, whether the adjustment to the determined navigational maneuver was due to a transient condition; and updating the predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition. 
     In some embodiments of the computer-readable medium, the predetermined model representative of at least one road segment may include a three-dimensional spline representing a predetermined path of travel along the at least one road segment. Updating the predetermined model may include an update to the three-dimensional spline representing a predetermined path of travel along the at least one road segment. The transient condition may be associated with a parked car, an intervening car, a pedestrian, a low light condition, a glare condition, a temporary barrier, or temporary roadwork. 
     In some embodiments, a system for interacting with a plurality of autonomous vehicles may include a memory including a predetermined road model representative of at least one road segment; and at least one processor programmed to: selectively receive, from the plurality of autonomous vehicles, road environment information based on navigation by the plurality of autonomous vehicles through their respective road environments; determine whether one or more updates to the predetermined road model are required based on the road environment information; and update the predetermined road model to include the one or more updates. 
     In some embodiments of the system, the road model may include a three-dimensional spline representing a predetermined path of travel along the at least one road segment. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from a particular vehicle. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from a group of vehicles. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from vehicles traveling within a particular geographic region. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from vehicles based on a determined model confidence level associated with a particular geographic region. Selectively receiving the road environment information may include a limitation on information transmissions received from vehicles to only those transmissions that include a potential discrepancy with respect to at least one aspect of the predetermined road model. 
     In some embodiments, a method for interacting with a plurality of autonomous vehicles may include selectively receiving, from the plurality of autonomous vehicles, road environment information based on navigation by the plurality of autonomous vehicles through their respective road environments; determining whether one or more updates to the predetermined road model are required based on the road environment information; and updating the predetermined road model to include the one or more updates. 
     In some embodiments of the method, the road model may include a three-dimensional spline representing a predetermined path of travel along the at least one road segment. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from a particular vehicle. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from a group of vehicles. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from vehicles traveling within a particular geographic region. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from vehicles based on a determined model confidence level associated with a particular geographic region. Selectively receiving the road environment information may include a limitation on information transmissions received from vehicles to only those transmissions that include a potential discrepancy with respect to at least one aspect of the predetermined road model. 
     In some embodiments, a non-transitory computer-readable medium may include instructions that are executable by at least one processor to cause the at least one processor to perform a method. The method may include selectively receiving, from the plurality of autonomous vehicles, road environment information based on navigation by the plurality of autonomous vehicles through their respective road environments; determining whether one or more updates to the predetermined road model are required based on the road environment information; and updating the predetermined road model to include the one or more updates. 
     In some embodiments of the computer-readable medium, the road model may include a three-dimensional spline representing a predetermined path of travel along the at least one road segment. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from a particular vehicle. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from a group of vehicles. Selectively receiving the road environment information may include a limitation on a frequency of information transmissions received from vehicles traveling within a particular geographic region. Selectively receiving the road environment information may include a limitation on information transmissions received from vehicles to only those transmissions that include a potential discrepancy with respect to at least one aspect of the predetermined road model. 
     Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processing device and perform any of the methods described herein. 
     The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings: 
         FIG. 1  is a diagrammatic representation of an exemplary system consistent with the disclosed embodiments. 
         FIG. 2A  is a diagrammatic side view representation of an exemplary vehicle including a system consistent with the disclosed embodiments. 
         FIG. 2B  is a diagrammatic top view representation of the vehicle and system shown in  FIG. 2A  consistent with the disclosed embodiments. 
         FIG. 2C  is a diagrammatic top view representation of another embodiment of a vehicle including a system consistent with the disclosed embodiments. 
         FIG. 2D  is a diagrammatic top view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments. 
         FIG. 2E  is a diagrammatic top view representation of yet another embodiment of a vehicle including a system consistent with the disclosed embodiments. 
         FIG. 2F  is a diagrammatic representation of exemplary vehicle control systems consistent with the disclosed embodiments. 
         FIG. 3A  is a diagrammatic representation of an interior of a vehicle including a rearview mirror and a user interface for a vehicle imaging system consistent with the disclosed embodiments. 
         FIG. 3B  is an illustration of an example of a camera mount that is configured to be positioned behind a rearview mirror and against a vehicle windshield consistent with the disclosed embodiments. 
         FIG. 3C  is an illustration of the camera mount shown in  FIG. 3B  from a different perspective consistent with the disclosed embodiments. 
         FIG. 3D  is an illustration of an example of a camera mount that is configured to be positioned behind a rearview mirror and against a vehicle windshield consistent with the disclosed embodiments. 
         FIG. 4  is an exemplary block diagram of a memory configured to store instructions for performing one or more operations consistent with the disclosed embodiments. 
         FIG. 5A  is a flowchart showing an exemplary process for causing one or more navigational responses based on monocular image analysis consistent with disclosed embodiments. 
         FIG. 5B  is a flowchart showing an exemplary process for detecting one or more vehicles and/or pedestrians in a set of images consistent with the disclosed embodiments. 
         FIG. 5C  is a flowchart showing an exemplary process for detecting road marks and/or lane geometry information in a set of images consistent with the disclosed embodiments. 
         FIG. 5D  is a flowchart showing an exemplary process for detecting traffic lights in a set of images consistent with the disclosed embodiments. 
         FIG. 5E  is a flowchart showing an exemplary process for causing one or more navigational responses based on a vehicle path consistent with the disclosed embodiments. 
         FIG. 5F  is a flowchart showing an exemplary process for determining whether a leading vehicle is changing lanes consistent with the disclosed embodiments. 
         FIG. 6  is a flowchart showing an exemplary process for causing one or more navigational responses based on stereo image analysis consistent with the disclosed embodiments. 
         FIG. 7  is a flowchart showing an exemplary process for causing one or more navigational responses based on an analysis of three sets of images consistent with the disclosed embodiments. 
         FIG. 8  shows a sparse map for providing autonomous vehicle navigation, consistent with the disclosed embodiments. 
         FIG. 9A  illustrates a polynomial representation of a portions of a road segment consistent with the disclosed embodiments. 
         FIG. 9B  illustrates a curve in three-dimensional space representing a target trajectory of a vehicle, for a particular road segment, included in a sparse map consistent with the disclosed embodiments. 
         FIG. 10  illustrates example landmarks that may be included in sparse map consistent with the disclosed embodiments. 
         FIG. 11A  shows polynomial representations of trajectories consistent with the disclosed embodiments. 
         FIGS. 11B and 11C  show target trajectories along a multi-lane road consistent with disclosed embodiments. 
         FIG. 11D  shows an example road signature profile consistent with disclosed embodiments. 
         FIG. 12  is a schematic illustration of a system that uses crowd sourcing data received from a plurality of vehicles for autonomous vehicle navigation, consistent with the disclosed embodiments. 
         FIG. 13  illustrates an example autonomous vehicle road navigation model represented by a plurality of three dimensional splines, consistent with the disclosed embodiments. 
         FIG. 14  illustrates a block diagram of a server consistent with the disclosed embodiments. 
         FIG. 15  illustrates a block diagram of a memory consistent with the disclosed embodiments. 
         FIG. 16  illustrates a process of clustering vehicle trajectories associated with vehicles, consistent with the disclosed embodiments. 
         FIG. 17  illustrates a navigation system for a vehicle, which may be used for autonomous navigation, consistent with the disclosed embodiments. 
         FIG. 18  is a flowchart showing an example process for processing vehicle navigation information for use in autonomous vehicle navigation, consistent with the disclosed embodiments. 
         FIG. 19  is a flowchart showing an example process performed by a navigation system of a vehicle, consistent with the disclosed embodiments. 
         FIG. 20  shows an example diagram of a memory consistent with the disclosed embodiments. 
         FIG. 21  is a flowchart illustrating an example process for uploading a recommended trajectory to a server consistent with the disclosed embodiments. 
         FIG. 22  illustrates an example environment including a system for identifying a landmark for use in autonomous vehicle navigation consistent with the disclosed embodiments. 
         FIG. 23  illustrates an example environment including a system for identifying a landmark for use in autonomous vehicle navigation consistent with the disclosed embodiments. 
         FIG. 24  illustrates a method of determining a condensed signature representation of a landmark consistent with the disclosed embodiments. 
         FIG. 25  illustrates another method of determining a condensed signature representation of a landmark consistent with the disclosed embodiments. 
         FIG. 26  illustrates an example block diagram of a memory consistent with the disclosed embodiments. 
         FIG. 27  is a flowchart showing an exemplary process for determining an identifier of a landmark consistent with the disclosed embodiments. 
         FIG. 28  is a flowchart showing an exemplary process for updating and distributing a vehicle road navigation model based on an identifier consistent with the disclosed embodiments. 
         FIG. 29  illustrates an example block diagram of a system for determining a location of a landmark for use in navigation of an autonomous vehicle consistent with the disclosed embodiments. 
         FIG. 30  illustrates an example block diagram of a memory consistent with the disclosed embodiments. 
         FIG. 31  illustrates an example scaling method for determining a distance from a vehicle to a landmark consistent with the disclosed embodiments. 
         FIG. 32  illustrates an example optical flow method for determining a distance from a vehicle to a landmark consistent with the disclosed embodiments. 
         FIG. 33A  is a flowchart showing an example process for determining a location of a landmark for use in navigation of an autonomous vehicle consistent with the disclosed embodiments. 
         FIG. 33B  is a flowchart showing an example process for measuring a position of a landmark for use in navigation of an autonomous vehicle consistent with the disclosed embodiments. 
         FIG. 34  is a diagrammatic top view representation of an exemplary vehicle including a system consistent with the disclosed embodiments in which the vehicle navigates using a landmark. 
         FIG. 35  is another diagrammatic top view representation of an exemplary vehicle including a system consistent with the disclosed embodiments in which the vehicle navigates using a landmark. 
         FIG. 36  is a flowchart showing an exemplary process for navigating an exemplary vehicle using a landmark. 
         FIG. 37  is a diagrammatic top view representation of an exemplary autonomous vehicle including a system consistent with the disclosed embodiments in which the autonomous vehicle navigates using tail alignment. 
         FIG. 38  is another diagrammatic top view representation of an exemplary autonomous vehicle including a system consistent with the disclosed embodiments in which the autonomous vehicle navigates using tail alignment. 
         FIG. 39  is a flowchart showing an exemplary process for navigating an exemplary autonomous vehicle using tail alignment. 
         FIG. 40  is a diagrammatic top view representation of an exemplary vehicle including a system consistent with the disclosed embodiments in which the vehicle navigates road junctions using two or more landmarks. 
         FIG. 41  is a flowchart showing an exemplary process for navigating an exemplary vehicle over road junctions using two or more landmarks. 
         FIG. 42  is a diagrammatic top view representation of an exemplary vehicle including a system consistent with the disclosed embodiments in which the vehicle navigates using overlapping maps. 
         FIGS. 43A, 43B, and 43C  are flowcharts showing an exemplary process for navigating an exemplary vehicle using overlapping maps. 
         FIG. 44  shows an exemplary remote server in communication a vehicle, consistent with the disclosed embodiments. 
         FIG. 45  shows a vehicle navigating along a multi-lane road, consistent with disclosed embodiments. 
         FIG. 46  shows a vehicle navigating using target trajectories along a multi-lane road, consistent with disclosed embodiments. 
         FIG. 47  shows an example of a road signature profile, consistent with the disclosed embodiments. 
         FIG. 48  illustrates an exemplary environment, consistent with the disclosed embodiments. 
         FIG. 49  is a flow chart showing an exemplary process for sparse map autonomous vehicle navigation, consistent with the disclosed embodiments 
         FIG. 50  illustrates an example environment for autonomous navigation based on an expected landmark location consistent with the disclosed embodiments. 
         FIG. 51  illustrates a configuration for autonomous navigation consistent with the disclosed embodiments. 
         FIG. 52  illustrates another example environment for autonomous navigation based on an expected landmark location consistent with the disclosed embodiments. 
         FIG. 53  illustrates another example environment for autonomous navigation based on an expected landmark location consistent with the disclosed embodiments. 
         FIG. 54  is a flow chart showing an exemplary process for autonomous navigation based on an expected landmark location consistent with the disclosed embodiments. 
         FIG. 55  is a diagrammatic representation of exemplary vehicle control systems consistent with the disclosed embodiments. 
         FIG. 56  is diagrammatic top view representation of an exemplary vehicle including a system consistent with the disclosed embodiments in which the vehicle navigates using lane width profiles or road width profiles. 
         FIG. 57  is graph showing an exemplary profile that may be used by the vehicle control systems consistent with the disclosed embodiments. 
         FIG. 58  is a diagrammatic top view representation of an exemplary vehicle including a system consistent with the disclosed embodiments in which the vehicle navigates using lengths or spacings of road markings on a road segment. 
         FIG. 59  is a diagrammatic top view representation of an exemplary vehicle including a system consistent with the disclosed embodiments in which the vehicle navigates using information regarding curvature of a road segment. 
         FIG. 60  is a flowchart showing an exemplary process for navigating an exemplary vehicle using road signatures. 
         FIG. 61A  is a diagrammatic side view representation of an exemplary vehicle consistent with disclosed embodiments. 
         FIG. 61B  is a diagrammatic side view representation of an exemplary vehicle consistent with disclosed embodiments. 
         FIG. 62  is a diagrammatic top view representation of an exemplary vehicle autonomously navigating on a road consistent with disclosed embodiments. 
         FIG. 63  is a flowchart showing an exemplary process for autonomously navigating a vehicle consistent with disclosed embodiments. 
         FIG. 64  is a diagrammatic perspective view of an environment captured by a forward facing image capture device on an exemplary vehicle consistent with disclosed embodiments. 
         FIG. 65  is an exemplary image received from a forward facing image capture device of a vehicle consistent with disclosed embodiments. 
         FIG. 66  is a flowchart showing an exemplary process for navigating a vehicle by determining a free space region in which the vehicle can travel consistent with disclosed embodiments. 
         FIG. 67  is a diagrammatic top view representation of an exemplary vehicle navigating on a road with snow covering at least some lane markings and road edges consistent with disclosed embodiments. 
         FIG. 68  is a flowchart showing an exemplary process for navigating a vehicle on a road with snow covering at least some lane markings and road edges consistent with disclosed embodiments. 
         FIG. 69  is a diagrammatic top view representation of an exemplary vehicle including a system for calibrating a speed of the vehicle consistent with disclosed embodiments. 
         FIG. 70  is a flowchart showing an exemplary process for calibrating a speed of a vehicle consistent with disclosed embodiments. 
         FIG. 71  is another diagrammatic top view representation of an exemplary vehicle including a system for calibrating a speed of the vehicle consistent with disclosed embodiments. 
         FIG. 72  is a flowchart showing another exemplary process for calibrating a speed of a vehicle consistent with disclosed embodiments. 
         FIG. 73  is an illustration of a street view of an exemplary road segment, consistent with disclosed embodiments. 
         FIG. 74  is an illustration of birds-eye view of an exemplary road segment, consistent with disclosed embodiments. 
         FIG. 75  is a flowchart showing an exemplary process for determining a lane assignment for a vehicle, consistent with disclosed embodiments. 
         FIG. 76  is an illustration of a street view of an exemplary road segment, consistent with disclosed embodiments. 
         FIG. 77A  is an illustration of birds-eye view of an exemplary road segment, consistent with disclosed embodiments. 
         FIG. 77B  is an illustration of a street view of an exemplary road segment consistent with disclosed embodiments. 
         FIG. 78  is a flowchart showing an exemplary process for autonomously navigating a vehicle along a road segment, consistent with disclosed embodiments. 
       IG.  79 A illustrates a plan view of a vehicle traveling on a roadway approaching wintery and icy road conditions at a particular location consistent with disclosed embodiments. 
         FIG. 79B  illustrates a plan view of a vehicle traveling on a roadway approaching a pedestrian consistent with disclosed embodiments. 
         FIG. 79C  illustrates a plan view of a vehicle traveling on a roadway in close proximity to another vehicle consistent with disclosed embodiments. 
         FIG. 79D  illustrates a plan view of a vehicle traveling on a roadway in a lane that is ending consistent with disclosed embodiments. 
         FIG. 80  illustrates a diagrammatic side view representation of an exemplary vehicle including the system consistent with the disclosed embodiments. 
         FIG. 81  illustrates an example flowchart representing a method for adaptive navigation of a vehicle based on user intervention consistent with the disclosed embodiments. 
         FIG. 82A  illustrates a plan view of a vehicle traveling on a roadway with a parked car consistent with disclosed embodiments. 
         FIG. 82B  illustrates a plan view of a vehicle traveling on a roadway in a lane that is ending consistent with the disclosed embodiments. 
         FIG. 82C  illustrates a plan view of a vehicle traveling on a roadway approaching a pedestrian consistent with disclosed embodiments. 
         FIG. 82D  illustrates a plan view of a vehicle traveling on a roadway approaching an area of construction consistent with the disclosed embodiments. 
         FIG. 83  illustrates an example flowchart representing a method for self-aware navigation of a vehicle consistent with the disclosed embodiments. 
         FIG. 84A  illustrates a plan view of a vehicle traveling on a roadway with multiple parked cars consistent with the disclosed embodiments. 
         FIG. 84B  illustrates a plan view of a vehicle traveling on a roadway with a car intervening directly in front of the vehicle consistent with the disclosed embodiments. 
         FIG. 84C  illustrates a plan view of a vehicle traveling on a roadway with a temporary barrier directly in front of the vehicle consistent with the disclosed embodiments. 
         FIG. 84D  illustrates a plan view of a vehicle traveling on a roadway with temporary roadwork directly in front of the vehicle consistent with the disclosed embodiments. 
         FIG. 85A  illustrates a plan view of a vehicle traveling on a roadway with a pot hole directly in front of the vehicle consistent with the disclosed embodiments. 
         FIG. 85B  illustrates a plan view of a vehicle traveling on a roadway with an animal and a pedestrian crossing in front of a vehicle consistent with the disclosed embodiments. 
         FIG. 86  illustrates an example flowchart representing a method for an adaptive road model manager consistent with disclosed embodiments. 
         FIG. 87A  illustrates a plan view of a single vehicle traveling on an interstate roadway consistent with the disclosed embodiments. 
         FIG. 87B  illustrates a plan view of a group of vehicles traveling on a city roadway consistent with the disclosed embodiments. 
         FIG. 87C  illustrates a plan view of a vehicle traveling on a roadway within a particular rural geographic region consistent with the disclosed embodiments. 
         FIG. 87D  illustrates a plan view of a vehicle traveling on a roadway with a lane shift consistent with the disclosed embodiments. 
         FIG. 88  illustrates an example flowchart representing a method for road model management based on selective feedback consistent with the disclosed embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions or modifications may be made to the components illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims. 
     Autonomous Vehicle Overview 
     As used throughout this disclosure, the term “autonomous vehicle” refers to a vehicle capable of implementing at least one navigational change without driver input. A “navigational change” refers to a change in one or more of steering, braking, or acceleration of the vehicle. To be autonomous, a vehicle need not be fully automatic (e.g., fully operation without a driver or without driver input). Rather, an autonomous vehicle includes those that can operate under driver control during certain time periods and without driver control during other time periods. Autonomous vehicles may also include vehicles that control only some aspects of vehicle navigation, such as steering (e.g., to maintain a vehicle course between vehicle lane constraints), but may leave other aspects to the driver (e.g., braking). In some cases, autonomous vehicles may handle some or all aspects of braking, speed control, and/or steering of the vehicle. 
     As human drivers typically rely on visual cues and observations order to control a vehicle, transportation infrastructures are built accordingly, with lane markings, traffic signs, and traffic lights are all designed to provide visual information to drivers. In view of these design characteristics of transportation infrastructures, an autonomous vehicle may include a camera and a processing unit that analyzes visual information captured from the environment of the vehicle. The visual information may include, for example, components of the transportation infrastructure (e.g., lane markings, traffic signs, traffic lights, etc.) that are observable by drivers and other obstacles (e.g., other vehicles, pedestrians, debris, etc.). Additionally, an autonomous vehicle may also use stored information, such as information that provides a model of the vehicle&#39;s environment when navigating. For example, the vehicle may use GPS data, sensor data (e.g., from an accelerometer, a speed sensor, a suspension sensor, etc.), and/or other map data to provide information related to its environment while it is traveling, and the vehicle (as well as other vehicles) may use the information to localize itself on the model. 
     In some embodiments in this disclosure, an autonomous vehicle may use information obtained while navigating (e.g., from a camera, GPS device, an accelerometer, a speed sensor, a suspension sensor, etc.). In other embodiments, an autonomous vehicle may use information obtained from past navigations by the vehicle (or by other vehicles) while navigating. In yet other embodiments, an autonomous vehicle may use a combination of information obtained while navigating and information obtained from past navigations. The following sections provide an overview of a system consistent with the disclosed embodiments, following by an overview of a forward-facing imaging system and methods consistent with the system. The sections that follow disclose systems and methods for constructing, using, and updating a sparse map for autonomous vehicle navigation. 
     System Overview 
       FIG. 1  is a block diagram representation of a system  100  consistent with the exemplary disclosed embodiments. System  100  may include various components depending on the requirements of a particular implementation. In some embodiments, system  100  may include a processing unit  110 , an image acquisition unit  120 , a position sensor  130 , one or more memory units  140 ,  150 , a map database  160 , a user interface  170 , and a wireless transceiver  172 . Processing unit  110  may include one or more processing devices. In some embodiments, processing unit  110  may include an applications processor  180 , an image processor  190 , or any other suitable processing device. Similarly, image acquisition unit  120  may include any number of image acquisition devices and components depending on the requirements of a particular application. In some embodiments, image acquisition unit  120  may include one or more image capture devices (e.g., cameras), such as image capture device  122 , image capture device  124 , and image capture device  126 . System  100  may also include a data interface  128  communicatively connecting processing device  110  to image acquisition device  120 . For example, data interface  128  may include any wired and/or wireless link or links for transmitting image data acquired by image accusation device  120  to processing unit  110 . 
     Wireless transceiver  172  may include one or more devices configured to exchange transmissions over an air interface to one or more networks (e.g., cellular, the Internet, etc.) by use of a radio frequency, infrared frequency, magnetic field, or an electric field. Wireless transceiver  172  may use any known standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.). 
     Both applications processor  180  and image processor  190  may include various types of processing devices. For example, either or both of applications processor  180  and image processor  190  may include a microprocessor, preprocessors (such as an image preprocessor), graphics processors, a central processing unit (CPU), support circuits, digital signal processors, integrated circuits, memory, or any other types of devices suitable for running applications and for image processing and analysis. In some embodiments, applications processor  180  and/or image processor  190  may include any type of single or multi-core processor, mobile device microcontroller, central processing unit, etc. Various processing devices may be used, including, for example, processors available from manufacturers such as Intel®, AMD®, etc. and may include various architectures (e.g., x86 processor, ARM®, etc.). 
     In some embodiments, applications processor  180  and/or image processor  190  may include any of the EyeQ series of processor chips available from Mobileye®. These processor designs each include multiple processing units with local memory and instruction sets. Such processors may include video inputs for receiving image data from multiple image sensors and may also include video out capabilities. In one example, the EyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2® architecture consists of two floating point, hyper-thread 32-bit RISC CPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), three Vector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller, 128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bit Video output controllers, 16 channels DMA and several peripherals. The MIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the second MIPS34K CPU and the multi-channel DMA as well as the other peripherals. The five VCEs, three VMP® and the MIPS34K CPU can perform intensive vision computations required by multi-function bundle applications. In another example, the EyeQ3®, which is a third generation processor and is six times more powerful that the EyeQ2®, may be used in the disclosed embodiments. 
     Any of the processing devices disclosed herein may be configured to perform certain functions. Configuring a processing device, such as any of the described EyeQ processors or other controller or microprocessor, to perform certain functions may include programming of computer executable instructions and making those instructions available to the processing device for execution during operation of the processing device. In some embodiments, configuring a processing device may include programming the processing device directly with architectural instructions. In other embodiments, configuring a processing device may include storing executable instructions on a memory that is accessible to the processing device during operation. For example, the processing device may access the memory to obtain and execute the stored instructions during operation. 
     While  FIG. 1  depicts two separate processing devices included in processing unit  110 , more or fewer processing devices may be used. For example, in some embodiments, a single processing device may be used to accomplish the tasks of applications processor  180  and image processor  190 . In other embodiments, these tasks may be performed by more than two processing devices. Further, in some embodiments, system  100  may include one or more of processing unit  110  without including other components, such as image acquisition unit  120 . 
     Processing unit  110  may comprise various types of devices. For example, processing unit  110  may include various devices, such as a controller, an image preprocessor, a central processing unit (CPU), support circuits, digital signal processors, integrated circuits, memory, or any other types of devices for image processing and analysis. The image preprocessor may include a video processor for capturing, digitizing and processing the imagery from the image sensors. The CPU may comprise any number of microcontrollers or microprocessors. The support circuits may be any number of circuits generally well known in the art, including cache, power supply, clock and input-output circuits. The memory may store software that, when executed by the processor, controls the operation of the system. The memory may include databases and image processing software. The memory may comprise any number of random access memories, read only memories, flash memories, disk drives, optical storage, tape storage, removable storage and other types of storage. In one instance, the memory may be separate from the processing unit  110 . In another instance, the memory may be integrated into the processing unit  110 . 
     Each memory  140 ,  150  may include software instructions that when executed by a processor (e.g., applications processor  180  and/or image processor  190 ), may control operation of various aspects of system  100 . These memory units may include various databases and image processing software. The memory units may include random access memory, read only memory, flash memory, disk drives, optical storage, tape storage, removable storage and/or any other types of storage. In some embodiments, memory units  140 ,  150  may be separate from the applications processor  180  and/or image processor  190 . In other embodiments, these memory units may be integrated into applications processor  180  and/or image processor  190 . 
     Position sensor  130  may include any type of device suitable for determining a location associated with at least one component of system  100 . In some embodiments, position sensor  130  may include a GPS receiver. Such receivers can determine a user position and velocity by processing signals broadcasted by global positioning system satellites. Position information from position sensor  130  may be made available to applications processor  180  and/or image processor  190 . 
     In some embodiments, system  100  may include components such as a speed sensor (e.g., a tachometer) for measuring a speed of vehicle  200  and/or an accelerometer for measuring acceleration of vehicle  200 . 
     User interface  170  may include any device suitable for providing information to or for receiving inputs from one or more users of system  100 . In some embodiments, user interface  170  may include user input devices, including, for example, a touchscreen, microphone, keyboard, pointer devices, track wheels, cameras, knobs, buttons, etc. With such input devices, a user may be able to provide information inputs or commands to system  100  by typing instructions or information, providing voice commands, selecting menu options on a screen using buttons, pointers, or eye-tracking capabilities, or through any other suitable techniques for communicating information to system  100 . 
     User interface  170  may be equipped with one or more processing devices configured to provide and receive information to or from a user and process that information for use by, for example, applications processor  180 . In some embodiments, such processing devices may execute instructions for recognizing and tracking eye movements, receiving and interpreting voice commands, recognizing and interpreting touches and/or gestures made on a touchscreen, responding to keyboard entries or menu selections, etc. In some embodiments, user interface  170  may include a display, speaker, tactile device, and/or any other devices for providing output information to a user. 
     Map database  160  may include any type of database for storing map data useful to system  100 . In some embodiments, map database  160  may include data relating to the position, in a reference coordinate system, of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc. Map database  160  may store not only the locations of such items, but also descriptors relating to those items, including, for example, names associated with any of the stored features. In some embodiments, map database  160  may be physically located with other components of system  100 . Alternatively or additionally, map database  160  or a portion thereof may be located remotely with respect to other components of system  100  (e.g., processing unit  110 ). In such embodiments, information from map database  160  may be downloaded over a wired or wireless data connection to a network (e.g., over a cellular network and/or the Internet, etc.). 
     Image capture devices  122 ,  124 , and  126  may each include any type of device suitable for capturing at least one image from an environment. Moreover, any number of image capture devices may be used to acquire images for input to the image processor. Some embodiments may include only a single image capture device, while other embodiments may include two, three, or even four or more image capture devices. Image capture devices  122 ,  124 , and  126  will be further described with reference to  FIGS. 2B-2E , below. 
     System  100 , or various components thereof, may be incorporated into various different platforms. In some embodiments, system  100  may be included on a vehicle  200 , as shown in  FIG. 2A . For example, vehicle  200  may be equipped with a processing unit  110  and any of the other components of system  100 , as described above relative to  FIG. 1 . While in some embodiments vehicle  200  may be equipped with only a single image capture device (e.g., camera), in other embodiments, such as those discussed in connection with  FIGS. 2B-2E , multiple image capture devices may be used. For example, either of image capture devices  122  and  124  of vehicle  200 , as shown in  FIG. 2A , may be part of an ADAS (Advanced Driver Assistance Systems) imaging set. 
     The image capture devices included on vehicle  200  as part of the image acquisition unit  120  may be positioned at any suitable location. In some embodiments, as shown in  FIGS. 2A-2E and 3A-3C , image capture device  122  may be located in the vicinity of the rearview mirror. This position may provide a line of sight similar to that of the driver of vehicle  200 , which may aid in determining what is and is not visible to the driver. Image capture device  122  may be positioned at any location near the rearview mirror, but placing image capture device  122  on the driver side of the mirror may further aid in obtaining images representative of the driver&#39;s field of view and/or line of sight. 
     Other locations for the image capture devices of image acquisition unit  120  may also be used. For example, image capture device  124  may be located on or in a bumper of vehicle  200 . Such a location may be especially suitable for image capture devices having a wide field of view. The line of sight of bumper-located image capture devices can be different from that of the driver and, therefore, the bumper image capture device and driver may not always see the same objects. The image capture devices (e.g., image capture devices  122 ,  124 , and  126 ) may also be located in other locations. For example, the image capture devices may be located on or in one or both of the side mirrors of vehicle  200 , on the roof of vehicle  200 , on the hood of vehicle  200 , on the trunk of vehicle  200 , on the sides of vehicle  200 , mounted on, positioned behind, or positioned in front of any of the windows of vehicle  200 , and mounted in or near light figures on the front and/or back of vehicle  200 , etc. 
     In addition to image capture devices, vehicle  200  may include various other components of system  100 . For example, processing unit  110  may be included on vehicle  200  either integrated with or separate from an engine control unit (ECU) of the vehicle. Vehicle  200  may also be equipped with a position sensor  130 , such as a GPS receiver and may also include a map database  160  and memory units  140  and  150 . 
     As discussed earlier, wireless transceiver  172  may and/or receive data over one or more networks (e.g., cellular networks, the Internet, etc.). For example, wireless transceiver  172  may upload data collected by system  100  to one or more servers, and download data from the one or more servers. Via wireless transceiver  172 , system  100  may receive, for example, periodic or on demand updates to data stored in map database  160 , memory  140 , and/or memory  150 . Similarly, wireless transceiver  172  may upload any data (e.g., images captured by image acquisition unit  120 , data received by position sensor  130  or other sensors, vehicle control systems, etc.) from by system  100  and/or any data processed by processing unit  110  to the one or more servers. 
     System  100  may upload data to a server (e.g., to the cloud) based on a privacy level setting. For example, system  100  may implement privacy level settings to regulate or limit the types of data (including metadata) sent to the server that may uniquely identify a vehicle and or driver/owner of a vehicle. Such settings may be set by user via, for example, wireless transceiver  172 , be initialized by factory default settings, or by data received by wireless transceiver  172 . 
     In some embodiments, system  100  may upload data according to a “high” privacy level, and under setting a setting, system  100  may transmit data (e.g., location information related to a route, captured images, etc.) without any details about the specific vehicle and/or driver/owner. For example, when uploading data according to a “high” privacy setting, system  100  may not include a vehicle identification number (VIN) or a name of a driver or owner of the vehicle, and may instead of transmit data, such as captured images and/or limited location information related to a route. 
     Other privacy levels are contemplated. For example, system  100  may transmit data to a server according to an “intermediate” privacy level and include additional information not included under a “high” privacy level, such as a make and/or model of a vehicle and/or a vehicle type (e.g., a passenger vehicle, sport utility vehicle, truck, etc.). In some embodiments, system  100  may upload data according to a “low” privacy level. Under a “low” privacy level setting, system  100  may upload data and include information sufficient to uniquely identify a specific vehicle, owner/driver, and/or a portion or entirely of a route traveled by the vehicle. Such “low” privacy level data may include one or more of, for example, a VIN, a driver/owner name, an origination point of a vehicle prior to departure, an intended destination of the vehicle, a make and/or model of the vehicle, a type of the vehicle, etc. 
       FIG. 2A  is a diagrammatic side view representation of an exemplary vehicle imaging system consistent with the disclosed embodiments.  FIG. 2B  is a diagrammatic top view illustration of the embodiment shown in  FIG. 2A . As illustrated in  FIG. 2B , the disclosed embodiments may include a vehicle  200  including in its body a system  100  with a first image capture device  122  positioned in the vicinity of the rearview mirror and/or near the driver of vehicle  200 , a second image capture device  124  positioned on or in a bumper region (e.g., one of bumper regions  210 ) of vehicle  200 , and a processing unit  110 . 
     As illustrated in  FIG. 2C , image capture devices  122  and  124  may both be positioned in the vicinity of the rearview mirror and/or near the driver of vehicle  200 . Additionally, while two image capture devices  122  and  124  are shown in  FIGS. 2B and 2C , it should be understood that other embodiments may include more than two image capture devices. For example, in the embodiments shown in  FIGS. 2D and 2E , first, second, and third image capture devices  122 ,  124 , and  126 , are included in the system  100  of vehicle  200 . 
     As illustrated in  FIG. 2D , image capture device  122  may be positioned in the vicinity of the rearview mirror and/or near the driver of vehicle  200 , and image capture devices  124  and  126  may be positioned on or in a bumper region (e.g., one of bumper regions  210 ) of vehicle  200 . And as shown in  FIG. 2E , image capture devices  122 ,  124 , and  126  may be positioned in the vicinity of the rearview mirror and/or near the driver seat of vehicle  200 . The disclosed embodiments are not limited to any particular number and configuration of the image capture devices, and the image capture devices may be positioned in any appropriate location within and/or on vehicle  200 . 
     It is to be understood that the disclosed embodiments are not limited to vehicles and could be applied in other contexts. It is also to be understood that disclosed embodiments are not limited to a particular type of vehicle  200  and may be applicable to all types of vehicles including automobiles, trucks, trailers, and other types of vehicles. 
     The first image capture device  122  may include any suitable type of image capture device. Image capture device  122  may include an optical axis. In one instance, the image capture device  122  may include an Aptina M9V024 WVGA sensor with a global shutter. In other embodiments, image capture device  122  may provide a resolution of 1280×960 pixels and may include a rolling shutter. Image capture device  122  may include various optical elements. In some embodiments one or more lenses may be included, for example, to provide a desired focal length and field of view for the image capture device. In some embodiments, image capture device  122  may be associated with a 6 mm lens or a 12 mm lens. In some embodiments, image capture device  122  may be configured to capture images having a desired field-of-view (FOV)  202 , as illustrated in  FIG. 2D . For example, image capture device  122  may be configured to have a regular FOV, such as within a range of 40 degrees to 56 degrees, including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater. Alternatively, image capture device  122  may be configured to have a narrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or 36 degree FOV. In addition, image capture device  122  may be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, image capture device  122  may include a wide angle bumper camera or one with up to a 180 degree FOV. In some embodiments, image capture device  122  may be a 7.2M pixel image capture device with an aspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100 degree horizontal FOV. Such an image capture device may be used in place of a three image capture device configuration. Due to significant lens distortion, the vertical FOV of such an image capture device may be significantly less than 50 degrees in implementations in which the image capture device uses a radially symmetric lens. For example, such a lens may not be radially symmetric which would allow for a vertical FOV greater than 50 degrees with 100 degree horizontal FOV. 
     The first image capture device  122  may acquire a plurality of first images relative to a scene associated with the vehicle  200 . Each of the plurality of first images may be acquired as a series of image scan lines, which may be captured using a rolling shutter. Each scan line may include a plurality of pixels. 
     The first image capture device  122  may have a scan rate associated with acquisition of each of the first series of image scan lines. The scan rate may refer to a rate at which an image sensor can acquire image data associated with each pixel included in a particular scan line. 
     Image capture devices  122 ,  124 , and  126  may contain any suitable type and number of image sensors, including CCD sensors or CMOS sensors, for example. In one embodiment, a CMOS image sensor may be employed along with a rolling shutter, such that each pixel in a row is read one at a time, and scanning of the rows proceeds on a row-by-row basis until an entire image frame has been captured. In some embodiments, the rows may be captured sequentially from top to bottom relative to the frame. 
     In some embodiments, one or more of the image capture devices (e.g., image capture devices  122 ,  124 , and  126 ) disclosed herein may constitute a high resolution imager and may have a resolution greater than 5M pixel, 7M pixel, 10M pixel, or greater. 
     The use of a rolling shutter may result in pixels in different rows being exposed and captured at different times, which may cause skew and other image artifacts in the captured image frame. On the other hand, when the image capture device  122  is configured to operate with a global or synchronous shutter, all of the pixels may be exposed for the same amount of time and during a common exposure period. As a result, the image data in a frame collected from a system employing a global shutter represents a snapshot of the entire FOV (such as FOV  202 ) at a particular time. In contrast, in a rolling shutter application, each row in a frame is exposed and data is capture at different times. Thus, moving objects may appear distorted in an image capture device having a rolling shutter. This phenomenon will be described in greater detail below. 
     The second image capture device  124  and the third image capturing device  126  may be any type of image capture device. Like the first image capture device  122 , each of image capture devices  124  and  126  may include an optical axis. In one embodiment, each of image capture devices  124  and  126  may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of image capture devices  124  and  126  may include a rolling shutter. Like image capture device  122 , image capture devices  124  and  126  may be configured to include various lenses and optical elements. In some embodiments, lenses associated with image capture devices  124  and  126  may provide FOVs (such as FOVs  204  and  206 ) that are the same as, or narrower than, a FOV (such as FOV  202 ) associated with image capture device  122 . For example, image capture devices  124  and  126  may have FOVs of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less. 
     Image capture devices  124  and  126  may acquire a plurality of second and third images relative to a scene associated with the vehicle  200 . Each of the plurality of second and third images may be acquired as a second and third series of image scan lines, which may be captured using a rolling shutter. Each scan line or row may have a plurality of pixels. Image capture devices  124  and  126  may have second and third scan rates associated with acquisition of each of image scan lines included in the second and third series. 
     Each image capture device  122 ,  124 , and  126  may be positioned at any suitable position and orientation relative to vehicle  200 . The relative positioning of the image capture devices  122 ,  124 , and  126  may be selected to aid in fusing together the information acquired from the image capture devices. For example, in some embodiments, a FOV (such as FOV  204 ) associated with image capture device  124  may overlap partially or fully with a FOV (such as FOV  202 ) associated with image capture device  122  and a FOV (such as FOV  206 ) associated with image capture device  126 . 
     Image capture devices  122 ,  124 , and  126  may be located on vehicle  200  at any suitable relative heights. In one instance, there may be a height difference between the image capture devices  122 ,  124 , and  126 , which may provide sufficient parallax information to enable stereo analysis. For example, as shown in  FIG. 2A , the two image capture devices  122  and  124  are at different heights. There may also be a lateral displacement difference between image capture devices  122 ,  124 , and  126 , giving additional parallax information for stereo analysis by processing unit  110 , for example. The difference in the lateral displacement may be denoted by d x , as shown in  FIGS. 2C and 2D . In some embodiments, fore or aft displacement (e.g., range displacement) may exist between image capture devices  122 ,  124 , and  126 . For example, image capture device  122  may be located 0.5 to 2 meters or more behind image capture device  124  and/or image capture device  126 . This type of displacement may enable one of the image capture devices to cover potential blind spots of the other image capture device(s). 
     Image capture devices  122  may have any suitable resolution capability (e.g., number of pixels associated with the image sensor), and the resolution of the image sensor(s) associated with the image capture device  122  may be higher, lower, or the same as the resolution of the image sensor(s) associated with image capture devices  124  and  126 . In some embodiments, the image sensor(s) associated with image capture device  122  and/or image capture devices  124  and  126  may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution. 
     The frame rate (e.g., the rate at which an image capture device acquires a set of pixel data of one image frame before moving on to capture pixel data associated with the next image frame) may be controllable. The frame rate associated with image capture device  122  may be higher, lower, or the same as the frame rate associated with image capture devices  124  and  126 . The frame rate associated with image capture devices  122 ,  124 , and  126  may depend on a variety of factors that may affect the timing of the frame rate. For example, one or more of image capture devices  122 ,  124 , and  126  may include a selectable pixel delay period imposed before or after acquisition of image data associated with one or more pixels of an image sensor in image capture device  122 ,  124 , and/or  126 . Generally, image data corresponding to each pixel may be acquired according to a clock rate for the device (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of image capture devices  122 ,  124 , and  126  may include a selectable horizontal blanking period imposed before or after acquisition of image data associated with a row of pixels of an image sensor in image capture device  122 ,  124 , and/or  126 . Further, one or more of image capture devices  122 ,  124 , and/or  126  may include a selectable vertical blanking period imposed before or after acquisition of image data associated with an image frame of image capture device  122 ,  124 , and  126 . 
     These timing controls may enable synchronization of frame rates associated with image capture devices  122 ,  124 , and  126 , even where the line scan rates of each are different. Additionally, as will be discussed in greater detail below, these selectable timing controls, among other factors (e.g., image sensor resolution, maximum line scan rates, etc.) may enable synchronization of image capture from an area where the FOV of image capture device  122  overlaps with one or more FOVs of image capture devices  124  and  126 , even where the field of view of image capture device  122  is different from the FOVs of image capture devices  124  and  126 . 
     Frame rate timing in image capture device  122 ,  124 , and  126  may depend on the resolution of the associated image sensors. For example, assuming similar line scan rates for both devices, if one device includes an image sensor having a resolution of 640×480 and another device includes an image sensor with a resolution of 1280×960, then more time will be required to acquire a frame of image data from the sensor having the higher resolution. 
     Another factor that may affect the timing of image data acquisition in image capture devices  122 ,  124 , and  126  is the maximum line scan rate. For example, acquisition of a row of image data from an image sensor included in image capture device  122 ,  124 , and  126  will require some minimum amount of time. Assuming no pixel delay periods are added, this minimum amount of time for acquisition of a row of image data will be related to the maximum line scan rate for a particular device. Devices that offer higher maximum line scan rates have the potential to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or more of image capture devices  124  and  126  may have a maximum line scan rate that is higher than a maximum line scan rate associated with image capture device  122 . In some embodiments, the maximum line scan rate of image capture device  124  and/or  126  may be 1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate of image capture device  122 . 
     In another embodiment, image capture devices  122 ,  124 , and  126  may have the same maximum line scan rate, but image capture device  122  may be operated at a scan rate less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices  124  and  126  operate at a line scan rate that is equal to the line scan rate of image capture device  122 . In other instances, the system may be configured such that the line scan rate of image capture device  124  and/or image capture device  126  may be 1.25, 1.5, 1.75, or 2 times or more than the line scan rate of image capture device  122 . 
     In some embodiments, image capture devices  122 ,  124 , and  126  may be asymmetric. That is, they may include cameras having different fields of view (FOV) and focal lengths. The fields of view of image capture devices  122 ,  124 , and  126  may include any desired area relative to an environment of vehicle  200 , for example. In some embodiments, one or more of image capture devices  122 ,  124 , and  126  may be configured to acquire image data from an environment in front of vehicle  200 , behind vehicle  200 , to the sides of vehicle  200 , or combinations thereof. 
     Further, the focal length associated with each image capture device  122 ,  124 , and/or  126  may be selectable (e.g., by inclusion of appropriate lenses etc.) such that each device acquires images of objects at a desired distance range relative to vehicle  200 . For example, in some embodiments image capture devices  122 ,  124 , and  126  may acquire images of close-up objects within a few meters from the vehicle. Image capture devices  122 ,  124 , and  126  may also be configured to acquire images of objects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Further, the focal lengths of image capture devices  122 ,  124 , and  126  may be selected such that one image capture device (e.g., image capture device  122 ) can acquire images of objects relatively close to the vehicle (e.g., within 10 m or within 20 m) while the other image capture devices (e.g., image capture devices  124  and  126 ) can acquire images of more distant objects (e.g., greater than 20 m, 50 m, 100 m, 150 m, etc.) from vehicle  200 . 
     According to some embodiments, the FOV of one or more image capture devices  122 ,  124 , and  126  may have a wide angle. For example, it may be advantageous to have a FOV of 140 degrees, especially for image capture devices  122 ,  124 , and  126  that may be used to capture images of the area in the vicinity of vehicle  200 . For example, image capture device  122  may be used to capture images of the area to the right or left of vehicle  200  and, in such embodiments, it may be desirable for image capture device  122  to have a wide FOV (e.g., at least 140 degrees). 
     The field of view associated with each of image capture devices  122 ,  124 , and  126  may depend on the respective focal lengths. For example, as the focal length increases, the corresponding field of view decreases. 
     Image capture devices  122 ,  124 , and  126  may be configured to have any suitable fields of view. In one particular example, image capture device  122  may have a horizontal FOV of 46 degrees, image capture device  124  may have a horizontal FOV of 23 degrees, and image capture device  126  may have a horizontal FOV in between 23 and 46 degrees. In another instance, image capture device  122  may have a horizontal FOV of 52 degrees, image capture device  124  may have a horizontal FOV of 26 degrees, and image capture device  126  may have a horizontal FOV in between 26 and 52 degrees. In some embodiments, a ratio of the FOV of image capture device  122  to the FOVs of image capture device  124  and/or image capture device  126  may vary from 1.5 to 2.0. In other embodiments, this ratio may vary between 1.25 and 2.25. 
     System  100  may be configured so that a field of view of image capture device  122  overlaps, at least partially or fully, with a field of view of image capture device  124  and/or image capture device  126 . In some embodiments, system  100  may be configured such that the fields of view of image capture devices  124  and  126 , for example, fall within (e.g., are narrower than) and share a common center with the field of view of image capture device  122 . In other embodiments, the image capture devices  122 ,  124 , and  126  may capture adjacent FOVs or may have partial overlap in their FOVs. In some embodiments, the fields of view of image capture devices  122 ,  124 , and  126  may be aligned such that a center of the narrower FOV image capture devices  124  and/or  126  may be located in a lower half of the field of view of the wider FOV device  122 . 
       FIG. 2F  is a diagrammatic representation of exemplary vehicle control systems, consistent with the disclosed embodiments. As indicated in  FIG. 2F , vehicle  200  may include throttling system  220 , braking system  230 , and steering system  240 . System  100  may provide inputs (e.g., control signals) to one or more of throttling system  220 , braking system  230 , and steering system  240  over one or more data links (e.g., any wired and/or wireless link or links for transmitting data). For example, based on analysis of images acquired by image capture devices  122 ,  124 , and/or  126 , system  100  may provide control signals to one or more of throttling system  220 , braking system  230 , and steering system  240  to navigate vehicle  200  (e.g., by causing an acceleration, a turn, a lane shift, etc.). Further, system  100  may receive inputs from one or more of throttling system  220 , braking system  230 , and steering system  24  indicating operating conditions of vehicle  200  (e.g., speed, whether vehicle  200  is braking and/or turning, etc.). Further details are provided in connection with  FIGS. 4-7 , below. 
     As shown in  FIG. 3A , vehicle  200  may also include a user interface  170  for interacting with a driver or a passenger of vehicle  200 . For example, user interface  170  in a vehicle application may include a touch screen  320 , knobs  330 , buttons  340 , and a microphone  350 . A driver or passenger of vehicle  200  may also use handles (e.g., located on or near the steering column of vehicle  200  including, for example, turn signal handles), buttons (e.g., located on the steering wheel of vehicle  200 ), and the like, to interact with system  100 . In some embodiments, microphone  350  may be positioned adjacent to a rearview mirror  310 . Similarly, in some embodiments, image capture device  122  may be located near rearview mirror  310 . In some embodiments, user interface  170  may also include one or more speakers  360  (e.g., speakers of a vehicle audio system). For example, system  100  may provide various notifications (e.g., alerts) via speakers  360 . 
       FIGS. 3B-3D  are illustrations of an exemplary camera mount  370  configured to be positioned behind a rearview mirror (e.g., rearview mirror  310 ) and against a vehicle windshield, consistent with disclosed embodiments. As shown in  FIG. 3B , camera mount  370  may include image capture devices  122 ,  124 , and  126 . Image capture devices  124  and  126  may be positioned behind a glare shield  380 , which may be flush against the vehicle windshield and include a composition of film and/or anti-reflective materials. For example, glare shield  380  may be positioned such that it aligns against a vehicle windshield having a matching slope. In some embodiments, each of image capture devices  122 ,  124 , and  126  may be positioned behind glare shield  380 , as depicted, for example, in  FIG. 3D . The disclosed embodiments are not limited to any particular configuration of image capture devices  122 ,  124 , and  126 , camera mount  370 , and glare shield  380 .  FIG. 3C  is an illustration of camera mount  370  shown in  FIG. 3B  from a front perspective. 
     As will be appreciated by a person skilled in the art having the benefit of this disclosure, numerous variations and/or modifications may be made to the foregoing disclosed embodiments. For example, not all components are essential for the operation of system  100 . Further, any component may be located in any appropriate part of system  100  and the components may be rearranged into a variety of configurations while providing the functionality of the disclosed embodiments. Therefore, the foregoing configurations are examples and, regardless of the configurations discussed above, system  100  can provide a wide range of functionality to analyze the surroundings of vehicle  200  and navigate vehicle  200  in response to the analysis. 
     As discussed below in further detail and consistent with various disclosed embodiments, system  100  may provide a variety of features related to autonomous driving and/or driver assist technology. For example, system  100  may analyze image data, position data (e.g., GPS location information), map data, speed data, and/or data from sensors included in vehicle  200 . System  100  may collect the data for analysis from, for example, image acquisition unit  120 , position sensor  130 , and other sensors. Further, system  100  may analyze the collected data to determine whether or not vehicle  200  should take a certain action, and then automatically take the determined action without human intervention. For example, when vehicle  200  navigates without human intervention, system  100  may automatically control the braking, acceleration, and/or steering of vehicle  200  (e.g., by sending control signals to one or more of throttling system  220 , braking system  230 , and steering system  240 ). Further, system  100  may analyze the collected data and issue warnings and/or alerts to vehicle occupants based on the analysis of the collected data. Additional details regarding the various embodiments that are provided by system  100  are provided below. 
     Forward-Facing Multi-Imaging System 
     As discussed above, system  100  may provide drive assist functionality that uses a multi-camera system. The multi-camera system may use one or more cameras facing in the forward direction of a vehicle. In other embodiments, the multi-camera system may include one or more cameras facing to the side of a vehicle or to the rear of the vehicle. In one embodiment, for example, system  100  may use a two-camera imaging system, where a first camera and a second camera (e.g., image capture devices  122  and  124 ) may be positioned at the front and/or the sides of a vehicle (e.g., vehicle  200 ). The first camera may have a field of view that is greater than, less than, or partially overlapping with, the field of view of the second camera. In addition, the first camera may be connected to a first image processor to perform monocular image analysis of images provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis of images provided by the second camera. The outputs (e.g., processed information) of the first and second image processors may be combined. In some embodiments, the second image processor may receive images from both the first camera and second camera to perform stereo analysis. In another embodiment, system  100  may use a three-camera imaging system where each of the cameras has a different field of view. Such a system may, therefore, make decisions based on information derived from objects located at varying distances both forward and to the sides of the vehicle. References to monocular image analysis may refer to instances where image analysis is performed based on images captured from a single point of view (e.g., from a single camera). Stereo image analysis may refer to instances where image analysis is performed based on two or more images captured with one or more variations of an image capture parameter. For example, captured images suitable for performing stereo image analysis may include images captured: from two or more different positions, from different fields of view, using different focal lengths, along with parallax information, etc. 
     For example, in one embodiment, system  100  may implement a three camera configuration using image capture devices  122 - 126 . In such a configuration, image capture device  122  may provide a narrow field of view (e.g., 34 degrees, or other values selected from a range of about 20 to 45 degrees, etc.), image capture device  124  may provide a wide field of view (e.g., 150 degrees or other values selected from a range of about 100 to about 180 degrees), and image capture device  126  may provide an intermediate field of view (e.g., 46 degrees or other values selected from a range of about 35 to about 60 degrees). In some embodiments, image capture device  126  may act as a main or primary camera. Image capture devices  122 - 126  may be positioned behind rearview mirror  310  and positioned substantially side-by-side (e.g., 6 cm apart). Further, in some embodiments, as discussed above, one or more of image capture devices  122 - 126  may be mounted behind glare shield  380  that is flush with the windshield of vehicle  200 . Such shielding may act to minimize the impact of any reflections from inside the car on image capture devices  122 - 126 . 
     In another embodiment, as discussed above in connection with  FIGS. 3B and 3C , the wide field of view camera (e.g., image capture device  124  in the above example) may be mounted lower than the narrow and main field of view cameras (e.g., image devices  122  and  126  in the above example). This configuration may provide a free line of sight from the wide field of view camera. To reduce reflections, the cameras may be mounted close to the windshield of vehicle  200 , and may include polarizers on the cameras to damp reflected light. 
     A three camera system may provide certain performance characteristics. For example, some embodiments may include an ability to validate the detection of objects by one camera based on detection results from another camera. In the three camera configuration discussed above, processing unit  110  may include, for example, three processing devices (e.g., three EyeQ series of processor chips, as discussed above), with each processing device dedicated to processing images captured by one or more of image capture devices  122 - 126 . 
     In a three camera system, a first processing device may receive images from both the main camera and the narrow field of view camera, and perform vision processing of the narrow FOV camera to, for example, detect other vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. Further, the first processing device may calculate a disparity of pixels between the images from the main camera and the narrow camera and create a 3D reconstruction of the environment of vehicle  200 . The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera. 
     The second processing device may receive images from main camera and perform vision processing to detect other vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. Additionally, the second processing device may calculate a camera displacement and, based on the displacement, calculate a disparity of pixels between successive images and create a 3D reconstruction of the scene (e.g., a structure from motion). The second processing device may send the structure from motion based 3D reconstruction to the first processing device to be combined with the stereo 3D images. 
     The third processing device may receive images from the wide FOV camera and process the images to detect vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. The third processing device may further execute additional processing instructions to analyze images to identify objects moving in the image, such as vehicles changing lanes, pedestrians, etc. 
     In some embodiments, having streams of image-based information captured and processed independently may provide an opportunity for providing redundancy in the system. Such redundancy may include, for example, using a first image capture device and the images processed from that device to validate and/or supplement information obtained by capturing and processing image information from at least a second image capture device. 
     In some embodiments, system  100  may use two image capture devices (e.g., image capture devices  122  and  124 ) in providing navigation assistance for vehicle  200  and use a third image capture device (e.g., image capture device  126 ) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices  122  and  124  may provide images for stereo analysis by system  100  for navigating vehicle  200 , while image capture device  126  may provide images for monocular analysis by system  100  to provide redundancy and validation of information obtained based on images captured from image capture device  122  and/or image capture device  124 . That is, image capture device  126  (and a corresponding processing device) may be considered to provide a redundant sub-system for providing a check on the analysis derived from image capture devices  122  and  124  (e.g., to provide an automatic emergency braking (AEB) system). 
     One of skill in the art will recognize that the above camera configurations, camera placements, number of cameras, camera locations, etc., are examples only. These components and others described relative to the overall system may be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding usage of a multi-camera system to provide driver assist and/or autonomous vehicle functionality follow below. 
       FIG. 4  is an exemplary functional block diagram of memory  140  and/or  150 , which may be stored/programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although the following refers to memory  140 , one of skill in the art will recognize that instructions may be stored in memory  140  and/or  150 . 
     As shown in  FIG. 4 , memory  140  may store a monocular image analysis module  402 , a stereo image analysis module  404 , a velocity and acceleration module  406 , and a navigational response module  408 . The disclosed embodiments are not limited to any particular configuration of memory  140 . Further, application processor  180  and/or image processor  190  may execute the instructions stored in any of modules  402 - 408  included in memory  140 . One of skill in the art will understand that references in the following discussions to processing unit  110  may refer to application processor  180  and image processor  190  individually or collectively. Accordingly, steps of any of the following processes may be performed by one or more processing devices. 
     In one embodiment, monocular image analysis module  402  may store instructions (such as computer vision software) which, when executed by processing unit  110 , performs monocular image analysis of a set of images acquired by one of image capture devices  122 ,  124 , and  126 . In some embodiments, processing unit  110  may combine information from a set of images with additional sensory information (e.g., information from radar) to perform the monocular image analysis. As described in connection with  FIGS. 5A-5D  below, monocular image analysis module  402  may include instructions for detecting a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other feature associated with an environment of a vehicle. Based on the analysis, system  100  (e.g., via processing unit  110 ) may cause one or more navigational responses in vehicle  200 , such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with navigational response module  408 . 
     In one embodiment, stereo image analysis module  404  may store instructions (such as computer vision software) which, when executed by processing unit  110 , performs stereo image analysis of first and second sets of images acquired by a combination of image capture devices selected from any of image capture devices  122 ,  124 , and  126 . In some embodiments, processing unit  110  may combine information from the first and second sets of images with additional sensory information (e.g., information from radar) to perform the stereo image analysis. For example, stereo image analysis module  404  may include instructions for performing stereo image analysis based on a first set of images acquired by image capture device  124  and a second set of images acquired by image capture device  126 . As described in connection with  FIG. 6  below, stereo image analysis module  404  may include instructions for detecting a set of features within the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and the like. Based on the analysis, processing unit  110  may cause one or more navigational responses in vehicle  200 , such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with navigational response module  408 . 
     In one embodiment, velocity and acceleration module  406  may store software configured to analyze data received from one or more computing and electromechanical devices in vehicle  200  that are configured to cause a change in velocity and/or acceleration of vehicle  200 . For example, processing unit  110  may execute instructions associated with velocity and acceleration module  406  to calculate a target speed for vehicle  200  based on data derived from execution of monocular image analysis module  402  and/or stereo image analysis module  404 . Such data may include, for example, a target position, velocity, and/or acceleration, the position and/or speed of vehicle  200  relative to a nearby vehicle, pedestrian, or road object, position information for vehicle  200  relative to lane markings of the road, and the like. In addition, processing unit  110  may calculate a target speed for vehicle  200  based on sensory input (e.g., information from radar) and input from other systems of vehicle  200 , such as throttling system  220 , braking system  230 , and/or steering system  240  of vehicle  200 . Based on the calculated target speed, processing unit  110  may transmit electronic signals to throttling system  220 , braking system  230 , and/or steering system  240  of vehicle  200  to trigger a change in velocity and/or acceleration by, for example, physically depressing the brake or easing up off the accelerator of vehicle  200 . 
     In one embodiment, navigational response module  408  may store software executable by processing unit  110  to determine a desired navigational response based on data derived from execution of monocular image analysis module  402  and/or stereo image analysis module  404 . Such data may include position and speed information associated with nearby vehicles, pedestrians, and road objects, target position information for vehicle  200 , and the like. Additionally, in some embodiments, the navigational response may be based (partially or fully) on map data, a predetermined position of vehicle  200 , and/or a relative velocity or a relative acceleration between vehicle  200  and one or more objects detected from execution of monocular image analysis module  402  and/or stereo image analysis module  404 . Navigational response module  408  may also determine a desired navigational response based on sensory input (e.g., information from radar) and inputs from other systems of vehicle  200 , such as throttling system  220 , braking system  230 , and steering system  240  of vehicle  200 . Based on the desired navigational response, processing unit  110  may transmit electronic signals to throttling system  220 , braking system  230 , and steering system  240  of vehicle  200  to trigger a desired navigational response by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of a predetermined angle. In some embodiments, processing unit  110  may use the output of navigational response module  408  (e.g., the desired navigational response) as an input to execution of velocity and acceleration module  406  for calculating a change in speed of vehicle  200 . 
       FIG. 5A  is a flowchart showing an exemplary process  500 A for causing one or more navigational responses based on monocular image analysis, consistent with disclosed embodiments. At step  510 , processing unit  110  may receive a plurality of images via data interface  128  between processing unit  110  and image acquisition unit  120 . For instance, a camera included in image acquisition unit  120  (such as image capture device  122  having field of view  202 ) may capture a plurality of images of an area forward of vehicle  200  (or to the sides or rear of a vehicle, for example) and transmit them over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.) to processing unit  110 . Processing unit  110  may execute monocular image analysis module  402  to analyze the plurality of images at step  520 , as described in further detail in connection with  FIGS. 5B-5D  below. By performing the analysis, processing unit  110  may detect a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, and the like. 
     Processing unit  110  may also execute monocular image analysis module  402  to detect various road hazards at step  520 , such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like. Road hazards may vary in structure, shape, size, and color, which may make detection of such hazards more challenging. In some embodiments, processing unit  110  may execute monocular image analysis module  402  to perform multi-frame analysis on the plurality of images to detect road hazards. For example, processing unit  110  may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit  110  may then use the 3D-map to detect the road surface, as well as hazards existing above the road surface. 
     At step  530 , processing unit  110  may execute navigational response module  408  to cause one or more navigational responses in vehicle  200  based on the analysis performed at step  520  and the techniques as described above in connection with  FIG. 4 . Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. In some embodiments, processing unit  110  may use data derived from execution of velocity and acceleration module  406  to cause the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof. For instance, processing unit  110  may cause vehicle  200  to shift one lane over and then accelerate by, for example, sequentially transmitting control signals to steering system  240  and throttling system  220  of vehicle  200 . Alternatively, processing unit  110  may cause vehicle  200  to brake while at the same time shifting lanes by, for example, simultaneously transmitting control signals to braking system  230  and steering system  240  of vehicle  200 . 
       FIG. 5B  is a flowchart showing an exemplary process  500 B for detecting one or more vehicles and/or pedestrians in a set of images, consistent with disclosed embodiments. Processing unit  110  may execute monocular image analysis module  402  to implement process  500 B. At step  540 , processing unit  110  may determine a set of candidate objects representing possible vehicles and/or pedestrians. For example, processing unit  110  may scan one or more images, compare the images to one or more predetermined patterns, and identify within each image possible locations that may contain objects of interest (e.g., vehicles, pedestrians, or portions thereof). The predetermined patterns may be designed in such a way to achieve a high rate of “false hits” and a low rate of “misses.” For example, processing unit  110  may use a low threshold of similarity to predetermined patterns for identifying candidate objects as possible vehicles or pedestrians. Doing so may allow processing unit  110  to reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian. 
     At step  542 , processing unit  110  may filter the set of candidate objects to exclude certain candidates (e.g., irrelevant or less relevant objects) based on classification criteria. Such criteria may be derived from various properties associated with object types stored in a database (e.g., a database stored in memory  140 ). Properties may include object shape, dimensions, texture, position (e.g., relative to vehicle  200 ), and the like. Thus, processing unit  110  may use one or more sets of criteria to reject false candidates from the set of candidate objects. 
     At step  544 , processing unit  110  may analyze multiple frames of images to determine whether objects in the set of candidate objects represent vehicles and/or pedestrians. For example, processing unit  110  may track a detected candidate object across consecutive frames and accumulate frame-by-frame data associated with the detected object (e.g., size, position relative to vehicle  200 , etc.). Additionally, processing unit  110  may estimate parameters for the detected object and compare the object&#39;s frame-by-frame position data to a predicted position. 
     At step  546 , processing unit  110  may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values (relative to vehicle  200 ) associated with the detected objects. In some embodiments, processing unit  110  may construct the measurements based on estimation techniques using a series of time-based observations such as Kalman filters or linear quadratic estimation (LQE), and/or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filters may be based on a measurement of an object&#39;s scale, where the scale measurement is proportional to a time to collision (e.g., the amount of time for vehicle  200  to reach the object). Thus, by performing steps  540 - 546 , processing unit  110  may identify vehicles and pedestrians appearing within the set of captured images and derive information (e.g., position, speed, size) associated with the vehicles and pedestrians. Based on the identification and the derived information, processing unit  110  may cause one or more navigational responses in vehicle  200 , as described in connection with  FIG. 5A , above. 
     At step  548 , processing unit  110  may perform an optical flow analysis of one or more images to reduce the probabilities of detecting a “false hit” and missing a candidate object that represents a vehicle or pedestrian. The optical flow analysis may refer to, for example, analyzing motion patterns relative to vehicle  200  in the one or more images associated with other vehicles and pedestrians, and that are distinct from road surface motion. Processing unit  110  may calculate the motion of candidate objects by observing the different positions of the objects across multiple image frames, which are captured at different times. Processing unit  110  may use the position and time values as inputs into mathematical models for calculating the motion of the candidate objects. Thus, optical flow analysis may provide another method of detecting vehicles and pedestrians that are nearby vehicle  200 . Processing unit  110  may perform optical flow analysis in combination with steps  540 - 546  to provide redundancy for detecting vehicles and pedestrians and increase the reliability of system  100 . 
       FIG. 5C  is a flowchart showing an exemplary process  500 C for detecting road marks and/or lane geometry information in a set of images, consistent with disclosed embodiments. Processing unit  110  may execute monocular image analysis module  402  to implement process  500 C. At step  550 , processing unit  110  may detect a set of objects by scanning one or more images. To detect segments of lane markings, lane geometry information, and other pertinent road marks, processing unit  110  may filter the set of objects to exclude those determined to be irrelevant (e.g., minor potholes, small rocks, etc.). At step  552 , processing unit  110  may group together the segments detected in step  550  belonging to the same road mark or lane mark. Based on the grouping, processing unit  110  may develop a model to represent the detected segments, such as a mathematical model. 
     At step  554 , processing unit  110  may construct a set of measurements associated with the detected segments. In some embodiments, processing unit  110  may create a projection of the detected segments from the image plane onto the real-world plane. The projection may be characterized using a  3 rd-degree polynomial having coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivative of the detected road. In generating the projection, processing unit  110  may take into account changes in the road surface, as well as pitch and roll rates associated with vehicle  200 . In addition, processing unit  110  may model the road elevation by analyzing position and motion cues present on the road surface. Further, processing unit  110  may estimate the pitch and roll rates associated with vehicle  200  by tracking a set of feature points in the one or more images. 
     At step  556 , processing unit  110  may perform multi-frame analysis by, for example, tracking the detected segments across consecutive image frames and accumulating frame-by-frame data associated with detected segments. As processing unit  110  performs multi-frame analysis, the set of measurements constructed at step  554  may become more reliable and associated with an increasingly higher confidence level. Thus, by performing steps  550 - 556 , processing unit  110  may identify road marks appearing within the set of captured images and derive lane geometry information. Based on the identification and the derived information, processing unit  110  may cause one or more navigational responses in vehicle  200 , as described in connection with  FIG. 5A , above. 
     At step  558 , processing unit  110  may consider additional sources of information to further develop a safety model for vehicle  200  in the context of its surroundings. Processing unit  110  may use the safety model to define a context in which system  100  may execute autonomous control of vehicle  200  in a safe manner. To develop the safety model, in some embodiments, processing unit  110  may consider the position and motion of other vehicles, the detected road edges and barriers, and/or general road shape descriptions extracted from map data (such as data from map database  160 ). By considering additional sources of information, processing unit  110  may provide redundancy for detecting road marks and lane geometry and increase the reliability of system  100 . 
       FIG. 5D  is a flowchart showing an exemplary process  500 D for detecting traffic lights in a set of images, consistent with disclosed embodiments. Processing unit  110  may execute monocular image analysis module  402  to implement process  500 D. At step  560 , processing unit  110  may scan the set of images and identify objects appearing at locations in the images likely to contain traffic lights. For example, processing unit  110  may filter the identified objects to construct a set of candidate objects, excluding those objects unlikely to correspond to traffic lights. The filtering may be done based on various properties associated with traffic lights, such as shape, dimensions, texture, position (e.g., relative to vehicle  200 ), and the like. Such properties may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit  110  may perform multi-frame analysis on the set of candidate objects reflecting possible traffic lights. For example, processing unit  110  may track the candidate objects across consecutive image frames, estimate the real-world position of the candidate objects, and filter out those objects that are moving (which are unlikely to be traffic lights). In some embodiments, processing unit  110  may perform color analysis on the candidate objects and identify the relative position of the detected colors appearing inside possible traffic lights. 
     At step  562 , processing unit  110  may analyze the geometry of a junction. The analysis may be based on any combination of: (i) the number of lanes detected on either side of vehicle  200 , (ii) markings (such as arrow marks) detected on the road, and (iii) descriptions of the junction extracted from map data (such as data from map database  160 ). Processing unit  110  may conduct the analysis using information derived from execution of monocular analysis module  402 . In addition, Processing unit  110  may determine a correspondence between the traffic lights detected at step  560  and the lanes appearing near vehicle  200 . 
     As vehicle  200  approaches the junction, at step  564 , processing unit  110  may update the confidence level associated with the analyzed junction geometry and the detected traffic lights. For instance, the number of traffic lights estimated to appear at the junction as compared with the number actually appearing at the junction may impact the confidence level. Thus, based on the confidence level, processing unit  110  may delegate control to the driver of vehicle  200  in order to improve safety conditions. By performing steps  560 - 564 , processing unit  110  may identify traffic lights appearing within the set of captured images and analyze junction geometry information. Based on the identification and the analysis, processing unit  110  may cause one or more navigational responses in vehicle  200 , as described in connection with  FIG. 5A , above. 
       FIG. 5E  is a flowchart showing an exemplary process  500 E for causing one or more navigational responses in vehicle  200  based on a vehicle path, consistent with the disclosed embodiments. At step  570 , processing unit  110  may construct an initial vehicle path associated with vehicle  200 . The vehicle path may be represented using a set of points expressed in coordinates (x, z), and the distance d i  between two points in the set of points may fall in the range of 1 to 5 meters. In one embodiment, processing unit  110  may construct the initial vehicle path using two polynomials, such as left and right road polynomials. Processing unit  110  may calculate the geometric midpoint between the two polynomials and offset each point included in the resultant vehicle path by a predetermined offset (e.g., a smart lane offset), if any (an offset of zero may correspond to travel in the middle of a lane). The offset may be in a direction perpendicular to a segment between any two points in the vehicle path. In another embodiment, processing unit  110  may use one polynomial and an estimated lane width to offset each point of the vehicle path by half the estimated lane width plus a predetermined offset (e.g., a smart lane offset). 
     At step  572 , processing unit  110  may update the vehicle path constructed at step  570 . Processing unit  110  may reconstruct the vehicle path constructed at step  570  using a higher resolution, such that the distance d k  between two points in the set of points representing the vehicle path is less than the distance d i  described above. For example, the distance d k  may fall in the range of 0.1 to 0.3 meters. Processing unit  110  may reconstruct the vehicle path using a parabolic spline algorithm, which may yield a cumulative distance vector S corresponding to the total length of the vehicle path (i.e., based on the set of points representing the vehicle path). 
     At step  574 , processing unit  110  may determine a look-ahead point (expressed in coordinates as (x l , z l )) based on the updated vehicle path constructed at step  572 . Processing unit  110  may extract the look-ahead point from the cumulative distance vector S, and the look-ahead point may be associated with a look-ahead distance and look-ahead time. The look-ahead distance, which may have a lower bound ranging from 10 to 20 meters, may be calculated as the product of the speed of vehicle  200  and the look-ahead time. For example, as the speed of vehicle  200  decreases, the look-ahead distance may also decrease (e.g., until it reaches the lower bound). The look-ahead time, which may range from 0.5 to 1.5 seconds, may be inversely proportional to the gain of one or more control loops associated with causing a navigational response in vehicle  200 , such as the heading error tracking control loop. For example, the gain of the heading error tracking control loop may depend on the bandwidth of a yaw rate loop, a steering actuator loop, car lateral dynamics, and the like. Thus, the higher the gain of the heading error tracking control loop, the lower the look-ahead time. 
     At step  576 , processing unit  110  may determine a heading error and yaw rate command based on the look-ahead point determined at step  574 . Processing unit  110  may determine the heading error by calculating the arc tangent of the look-ahead point, e.g., arc tan (x l /z l ). Processing unit  110  may determine the yaw rate command as the product of the heading error and a high-level control gain. The high-level control gain may be equal to: (2/look-ahead time), if the look-ahead distance is not at the lower bound. Otherwise, the high-level control gain may be equal to: (2*speed of vehicle  200 /look-ahead distance). 
       FIG. 5F  is a flowchart showing an exemplary process  500 F for determining whether a leading vehicle is changing lanes, consistent with the disclosed embodiments. At step  580 , processing unit  110  may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling ahead of vehicle  200 ). For example, processing unit  110  may determine the position, velocity (e.g., direction and speed), and/or acceleration of the leading vehicle, using the techniques described in connection with  FIGS. 5A and 5B , above. Processing unit  110  may also determine one or more road polynomials, a look-ahead point (associated with vehicle  200 ), and/or a snail trail (e.g., a set of points describing a path taken by the leading vehicle), using the techniques described in connection with  FIG. 5E , above. 
     At step  582 , processing unit  110  may analyze the navigation information determined at step  580 . In one embodiment, processing unit  110  may calculate the distance between a snail trail and a road polynomial (e.g., along the trail). If the variance of this distance along the trail exceeds a predetermined threshold (for example, 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curvy road, and 0.5 to 0.6 meters on a road with sharp curves), processing unit  110  may determine that the leading vehicle is likely changing lanes. In the case where multiple vehicles are detected traveling ahead of vehicle  200 , processing unit  110  may compare the snail trails associated with each vehicle. Based on the comparison, processing unit  110  may determine that a vehicle whose snail trail does not match with the snail trails of the other vehicles is likely changing lanes. Processing unit  110  may additionally compare the curvature of the snail trail (associated with the leading vehicle) with the expected curvature of the road segment in which the leading vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database  160 ), from road polynomials, from other vehicles&#39; snail trails, from prior knowledge about the road, and the like. If the difference in curvature of the snail trail and the expected curvature of the road segment exceeds a predetermined threshold, processing unit  110  may determine that the leading vehicle is likely changing lanes. 
     In another embodiment, processing unit  110  may compare the leading vehicle&#39;s instantaneous position with the look-ahead point (associated with vehicle  200 ) over a specific period of time (e.g., 0.5 to 1.5 seconds). If the distance between the leading vehicle&#39;s instantaneous position and the look-ahead point varies during the specific period of time, and the cumulative sum of variation exceeds a predetermined threshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curvy road, and 1.3 to 1.7 meters on a road with sharp curves), processing unit  110  may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit  110  may analyze the geometry of the snail trail by comparing the lateral distance traveled along the trail with the expected curvature of the snail trail. The expected radius of curvature may be determined according to the calculation: (δ z   2 +δ x   2 )/2/(δ x ), where δ x  represents the lateral distance traveled and δ z  represents the longitudinal distance traveled. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), processing unit  110  may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit  110  may analyze the position of the leading vehicle. If the position of the leading vehicle obscures a road polynomial (e.g., the leading vehicle is overlaid on top of the road polynomial), then processing unit  110  may determine that the leading vehicle is likely changing lanes. In the case where the position of the leading vehicle is such that, another vehicle is detected ahead of the leading vehicle and the snail trails of the two vehicles are not parallel, processing unit  110  may determine that the (closer) leading vehicle is likely changing lanes. 
     At step  584 , processing unit  110  may determine whether or not leading vehicle  200  is changing lanes based on the analysis performed at step  582 . For example, processing unit  110  may make the determination based on a weighted average of the individual analyses performed at step  582 . Under such a scheme, for example, a decision by processing unit  110  that the leading vehicle is likely changing lanes based on a particular type of analysis may be assigned a value of “1” (and “0” to represent a determination that the leading vehicle is not likely changing lanes). Different analyses performed at step  582  may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights. 
       FIG. 6  is a flowchart showing an exemplary process  600  for causing one or more navigational responses based on stereo image analysis, consistent with disclosed embodiments. At step  610 , processing unit  110  may receive a first and second plurality of images via data interface  128 . For example, cameras included in image acquisition unit  120  (such as image capture devices  122  and  124  having fields of view  202  and  204 ) may capture a first and second plurality of images of an area forward of vehicle  200  and transmit them over a digital connection (e.g., USB, wireless, Bluetooth, etc.) to processing unit  110 . In some embodiments, processing unit  110  may receive the first and second plurality of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configurations or protocols. 
     At step  620 , processing unit  110  may execute stereo image analysis module  404  to perform stereo image analysis of the first and second plurality of images to create a 3D map of the road in front of the vehicle and detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. Stereo image analysis may be performed in a manner similar to the steps described in connection with  FIGS. 5A-5D , above. For example, processing unit  110  may execute stereo image analysis module  404  to detect candidate objects (e.g., vehicles, pedestrians, road marks, traffic lights, road hazards, etc.) within the first and second plurality of images, filter out a subset of the candidate objects based on various criteria, and perform multi-frame analysis, construct measurements, and determine a confidence level for the remaining candidate objects. In performing the steps above, processing unit  110  may consider information from both the first and second plurality of images, rather than information from one set of images alone. For example, processing unit  110  may analyze the differences in pixel-level data (or other data subsets from among the two streams of captured images) for a candidate object appearing in both the first and second plurality of images. As another example, processing unit  110  may estimate a position and/or velocity of a candidate object (e.g., relative to vehicle  200 ) by observing that the object appears in one of the plurality of images but not the other or relative to other differences that may exist relative to objects appearing if the two image streams. For example, position, velocity, and/or acceleration relative to vehicle  200  may be determined based on trajectories, positions, movement characteristics, etc. of features associated with an object appearing in one or both of the image streams. 
     At step  630 , processing unit  110  may execute navigational response module  408  to cause one or more navigational responses in vehicle  200  based on the analysis performed at step  620  and the techniques as described above in connection with  FIG. 4 . Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, a change in velocity, braking, and the like. In some embodiments, processing unit  110  may use data derived from execution of velocity and acceleration module  406  to cause the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof. 
       FIG. 7  is a flowchart showing an exemplary process  700  for causing one or more navigational responses based on an analysis of three sets of images, consistent with disclosed embodiments. At step  710 , processing unit  110  may receive a first, second, and third plurality of images via data interface  128 . For instance, cameras included in image acquisition unit  120  (such as image capture devices  122 ,  124 , and  126  having fields of view  202 ,  204 , and  206 ) may capture a first, second, and third plurality of images of an area forward and/or to the side of vehicle  200  and transmit them over a digital connection (e.g., USB, wireless, Bluetooth, etc.) to processing unit  110 . In some embodiments, processing unit  110  may receive the first, second, and third plurality of images via three or more data interfaces. For example, each of image capture devices  122 ,  124 ,  126  may have an associated data interface for communicating data to processing unit  110 . The disclosed embodiments are not limited to any particular data interface configurations or protocols. 
     At step  720 , processing unit  110  may analyze the first, second, and third plurality of images to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. The analysis may be performed in a manner similar to the steps described in connection with  FIGS. 5A-5D and 6 , above. For instance, processing unit  110  may perform monocular image analysis (e.g., via execution of monocular image analysis module  402  and based on the steps described in connection with  FIGS. 5A-5D , above) on each of the first, second, and third plurality of images. Alternatively, processing unit  110  may perform stereo image analysis (e.g., via execution of stereo image analysis module  404  and based on the steps described in connection with  FIG. 6 , above) on the first and second plurality of images, the second and third plurality of images, and/or the first and third plurality of images. The processed information corresponding to the analysis of the first, second, and/or third plurality of images may be combined. In some embodiments, processing unit  110  may perform a combination of monocular and stereo image analyses. For example, processing unit  110  may perform monocular image analysis (e.g., via execution of monocular image analysis module  402 ) on the first plurality of images and stereo image analysis (e.g., via execution of stereo image analysis module  404 ) on the second and third plurality of images. The configuration of image capture devices  122 ,  124 , and  126 —including their respective locations and fields of view  202 ,  204 , and  206 —may influence the types of analyses conducted on the first, second, and third plurality of images. The disclosed embodiments are not limited to a particular configuration of image capture devices  122 ,  124 , and  126 , or the types of analyses conducted on the first, second, and third plurality of images. 
     In some embodiments, processing unit  110  may perform testing on system  100  based on the images acquired and analyzed at steps  710  and  720 . Such testing may provide an indicator of the overall performance of system  100  for certain configurations of image capture devices  122 ,  124 , and  126 . For example, processing unit  110  may determine the proportion of “false hits” (e.g., cases where system  100  incorrectly determined the presence of a vehicle or pedestrian) and “misses.” 
     At step  730 , processing unit  110  may cause one or more navigational responses in vehicle  200  based on information derived from two of the first, second, and third plurality of images. Selection of two of the first, second, and third plurality of images may depend on various factors, such as, for example, the number, types, and sizes of objects detected in each of the plurality of images. Processing unit  110  may also make the selection based on image quality and resolution, the effective field of view reflected in the images, the number of captured frames, the extent to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which an object appears, the proportion of the object that appears in each such frame, etc.), and the like. 
     In some embodiments, processing unit  110  may select information derived from two of the first, second, and third plurality of images by determining the extent to which information derived from one image source is consistent with information derived from other image sources. For example, processing unit  110  may combine the processed information derived from each of image capture devices  122 ,  124 , and  126  (whether by monocular analysis, stereo analysis, or any combination of the two) and determine visual indicators (e.g., lane markings, a detected vehicle and its location and/or path, a detected traffic light, etc.) that are consistent across the images captured from each of image capture devices  122 ,  124 , and  126 . Processing unit  110  may also exclude information that is inconsistent across the captured images (e.g., a vehicle changing lanes, a lane model indicating a vehicle that is too close to vehicle  200 , etc.). Thus, processing unit  110  may select information derived from two of the first, second, and third plurality of images based on the determinations of consistent and inconsistent information. 
     Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. Processing unit  110  may cause the one or more navigational responses based on the analysis performed at step  720  and the techniques as described above in connection with  FIG. 4 . Processing unit  110  may also use data derived from execution of velocity and acceleration module  406  to cause the one or more navigational responses. In some embodiments, processing unit  110  may cause the one or more navigational responses based on a relative position, relative velocity, and/or relative acceleration between vehicle  200  and an object detected within any of the first, second, and third plurality of images. Multiple navigational responses may occur simultaneously, in sequence, or any combination thereof. 
     Sparse Road Model for Autonomous Vehicle Navigation 
     In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigating an autonomous vehicle without storing and/or updating a large quantity of data. As discussed below in further detail, an autonomous vehicle may use the sparse map to navigate one or more roads based on one or more stored trajectories. 
     Sparse Map for Autonomous Vehicle Navigation 
     In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. For example, the sparse map may provide sufficient information for navigation without requiring excessive data storage or data transfer rates. As discussed below in further detail, a vehicle (which may be an autonomous vehicle) may use the sparse map to navigate one or more roads. For example, in some embodiments, the sparse map may include data related to a road and potentially landmarks along the road that may be sufficient for vehicle navigation, but which also exhibit small data footprints. For example, the sparse data maps described in detail below may require significantly less storage space and data transfer bandwidth as compared with digital maps including detailed map information, such as image data collected along a road. For example, rather than storing detailed representations of a road segment, the sparse data map may store three dimensional polynomial representations of preferred vehicle paths along a road. These paths may require very little data storage space. Further, in the described sparse data maps, landmarks may be identified and included in the sparse map road model to aid in navigation. These landmarks may be located at any spacing suitable for enabling vehicle navigation, but in some cases, such landmarks need not be identified and included in the model at high densities and short spacings. Rather, in some cases, navigation may be possible based on landmarks that are spaced apart by at least 50 meters, at least 100 meters, at least 500 meters, at least 1 kilometer, or at least 2 kilometers. As will be discussed in more detail in other sections, the sparse map may be generated based on data collected or measured by vehicles equipped with various sensors and devices, such as image capture devices, Global Positioning System sensors, motion sensors, etc., as the vehicles travel along roadways. In some cases, the sparse map may be generated based on data collected during multiple drives of one or more vehicles along a particular roadway. 
     Consistent with disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. At the core of the sparse maps, one or more three-dimensional contours may represent predetermined trajectories that autonomous vehicles may traverse as they move along associated road segments. The sparse maps may also include data representing one or more road features. Such road features may include recognized landmarks, road signature profiles, and any other road-related features useful in navigating a vehicle. The sparse maps may enable autonomous navigation of a vehicle based on relatively small amounts of data included in the sparse map. For example, rather than including detailed representations of a road, such as road edges, road curvature, images associated with road segments, or data detailing other physical features associated with a road segment, the disclosed embodiments of the sparse map may require relatively little storage space (and relatively little bandwidth when portions of the sparse map are transferred to a vehicle), but may still adequately provide for autonomous vehicle navigation. The small data footprint of the disclosed sparse maps, discussed in further detail below, may be achieved in some embodiments by storing representations of road-related elements that require small amounts of data, but still enable autonomous navigation. For example, rather than storing detailed representations of various aspects of a road, the disclosed sparse maps may store polynomial representations of one or more trajectories that a vehicle may follow along the road. Thus, rather than storing (or having to transfer) details regarding the physical nature of the road to enable navigation along the road, using the disclosed sparse maps, a vehicle may be navigated along a particular road segment without, in some cases, having to interpret physical aspects of the road, but rather, by aligning its path of travel with a trajectory (e.g., a polynomial spline) along the particular road segment. In this way, the vehicle may be navigated based mainly upon the stored trajectory (e.g., a polynomial spline) that may require much less storage space than an approach involving storage of roadway images, road parameters, road layout, etc. 
     In addition to the stored polynomial representations of trajectories along a road segment, the disclosed sparse maps may also include small data objects that may represent a road feature. In some embodiments, the small data objects may include digital signatures, which are derived from a digital image (or a digital signal) that was obtained by a sensor (e.g., a camera or other sensor, such as a suspension sensor) onboard a vehicle traveling along the road segment. The digital signature may have a reduced size relative to the signal that was acquired by the sensor. In some embodiments, the digital signature may be created to be compatible with a classifier function that is configured to detect and to identify the road feature from the signal that is acquired by the sensor, for example during a subsequent drive. In some embodiments, a digital signature may be created such that it has a footprint that is as small as possible, while retaining the ability to correlate or match the road feature with the stored signature based on an image (or a digital signal generated by a sensor, if the stored signature is not based on an image and/or includes other data) of the road feature that is captured by a camera onboard a vehicle traveling along the same road segment at a subsequent time. In some embodiments, a size of the data objects may be further associated with a uniqueness of the road feature. For example, for a road feature that is detectable by a camera onboard a vehicle, and where the camera system onboard the vehicle is coupled to a classifier which is capable of distinguishing the image data corresponding to that road feature as being associated with a particular type of road feature, for example, a road sign, and where such a road sign is locally unique in that area (e.g., there is no identical road sign or road sign of the same type nearby), it may be sufficient to store data indicating the type of the road feature and its location. 
     As will be discussed in further detail below, road features (e.g., landmarks along a road segment) may be stored as small data objects that may represent a road feature in relatively few bytes, while at the same time providing sufficient information for recognizing and using such a feature for navigation. In just one example, a road sign may be identified as a recognized landmark on which navigation of a vehicle may be based. A representation of the road sign may be stored in the sparse map to include, e.g., a few bytes of data indicating a type of landmark (e.g., a stop sign) and a few bytes of data indicating a location of the landmark. Navigating based on such data-light representations of the landmarks (e.g., using representations sufficient for locating, recognizing, and navigating based upon the landmarks) may provide a desired level of navigational functionality associated with sparse maps without significantly increasing the data overhead associated with the sparse maps. This lean representation of landmarks (and other road features) may take advantage of the sensors and processors included onboard such vehicles that are configured to detect, identify, and/or classify certain road features. When, for example, a sign or even a particular type of a sign is locally unique (e.g., when there is no other sign or no other sign of the same type) in a given area, the sparse map may use data indicating a type of a landmark (a sign or a specific type of sign), and during navigation (e.g., autonomous navigation) when a camera onboard an autonomous vehicle captures an image of the area including a sign (or of a specific type of sign), the processor may process the image, detect the sign (if indeed present in the image), classify it as a sign (or as a specific type of sign), and correlate its location with the location of the sign as stored in the sparse map. 
     In some embodiments, an autonomous vehicle may include a vehicle body and a processor configured to receive data included in a sparse map and generate navigational instructions for navigating the vehicle along a road segment based on the data in the sparse map. 
       FIG. 8  shows a sparse map  800  that vehicle  200  (which may be an autonomous vehicle) may access for providing autonomous vehicle navigation. Sparse map  800  may be stored in a memory, such as memory  140  or  150 . Such memory devices may include any types of non-transitory storage devices or computer-readable media. For example, in some embodiments, memory  140  or  150  may include hard drives, compact discs, flash memory, magnetic based memory devices, optical based memory devices, etc. In some embodiments, sparse map  800  may be stored in a database (e.g., map database  160 ) that may be stored in memory  140  or  150 , or other types of storage devices. 
     In some embodiments, sparse map  800  may be stored on a storage device or a non-transitory computer-readable medium provided onboard vehicle  200  (e.g., a storage device included in a navigation system onboard vehicle  200 ). A processor (e.g., processing unit  110 ) provided on vehicle  200  may access sparse map  800  stored in the storage device or computer-readable medium provided onboard vehicle  200  in order to generate navigational instructions for guiding the autonomous vehicle  200  as it traverses a road segment. 
     Sparse map  800  need not be stored locally with respect to a vehicle, however. In some embodiments, sparse map  800  may be stored on a storage device or computer-readable medium provided on a remote server that communicates with vehicle  200  or a device associated with vehicle  200 . A processor (e.g., processing unit  110 ) provided on vehicle  200  may receive data included in sparse map  800  from the remove server and may execute the data for guiding the autonomous driving of vehicle  200 . In such embodiments, sparse map  800  may be made accessible to a plurality of vehicles traversing various road segments (e.g., tens, hundreds, thousands, or millions of vehicles, etc.). It should be noted also that sparse map  800  may include multiple sub-maps. For example, in some embodiments, sparse map  800  may include hundreds, thousands, millions, or more, of sub-maps that can be used in navigating a vehicle. Such sub-maps may be referred to as local maps, and a vehicle traveling along a roadway may access any number of local maps relevant to a location in which the vehicle is traveling. The local map sections of sparse map  800  may be stored with a Global Navigation Satellite System (GNSS) key as an index to the database of sparse map  800 . Thus, while computation of steering angles for navigating a host vehicle in the present system may be performed without reliance upon a GNSS position of the host vehicle, road features, or landmarks, such GNSS information may be used for retrieval of relevant local maps. 
     Collection of data and generation of sparse map  800  is covered in detail in other sections. In general, however, sparse map  800  may be generated based on data collected from one or more vehicles as they travel along roadways. For example, using sensors aboard the one or more vehicles (e.g., cameras, speedometers, GPS, accelerometers, etc.), the trajectories that the one or more vehicles travel along a roadway may be recorded, and the polynomial representation of a preferred trajectory for vehicles making subsequent trips along the roadway may be determined based on the collected trajectories traveled by the one or more vehicles. Similarly, data collected by the one or more vehicles may aid in identifying potential landmarks along a particular roadway. Data collected from traversing vehicles may also be used to identify road profile information, such as road width profiles, road roughness profiles, traffic line spacing profiles, etc. Using the collected information, sparse map  800  may be generated and distributed (e.g., for local storage or via on-the-fly data transmission) for use in navigating one or more autonomous vehicles. Map generation may not end upon initial generation of the map, however. As will be discussed in greater detail in other sections, sparse map  800  may be continuously or periodically updated based on data collected from vehicles as those vehicles continue to traverse roadways included in sparse map  800 . 
     Data recorded in sparse map  800  may include position information based on Global Positioning System (GPS) data. For example, location information may be included in sparse map  800  for various map elements, including, for example, landmark locations, road profile locations, etc. Locations for map elements included in sparse map  800  may be obtained using GPS data collected from vehicles traversing a roadway. For example, a vehicle passing an identified landmark may determine a location of the identified landmark using GPS position information associated with the vehicle and a determination of a location of the identified landmark relative to the vehicle (e.g., based on image analysis of data collected from one or more cameras on board the vehicle). Such location determinations of an identified landmark (or any other feature included in sparse map  800 ) may be repeated as additional vehicles pass the location of the identified landmark. Some or all of the additional location determinations can be used to refine the location information stored in sparse map  800  relative to the identified landmark. For example, in some embodiments, multiple position measurements relative to a particular feature stored in sparse map  800  may be averaged together. Any other mathematical operations, however, may also be used to refine a stored location of a map element based on a plurality of determined locations for the map element. 
     The sparse map of the disclosed embodiments may enable autonomous navigation of a vehicle using relatively small amounts of stored data. In some embodiments, sparse map  800  may have a data density (e.g., including data representing the target trajectories, landmarks, and any other stored road features) of less than 2 MB per kilometer of roads, less than 1 MB per kilometer of roads, less than 500 kB per kilometer of roads, or less than 100 kB per kilometer of roads. In some embodiments, the data density of sparse map  800  may be less than 10 kB per kilometer of roads or even less than 2 kB per kilometer of roads (e.g., 1.6 kB per kilometer), or no more than 10 kB per kilometer of roads, or no more than 20 kB per kilometer of roads. In some embodiments, most if not all of the roadways of the United States may be navigated autonomously using a sparse map having a total of 4 GB or less of data. These data density values may represent an average over an entire sparse map  800 , over a local map within sparse map  800 , and/or over a particular road segment within sparse map  800 . 
     As noted, sparse map  800  may include representations of a plurality of target trajectories  810  for guiding autonomous driving or navigation along a road segment. Such target trajectories may be stored as three-dimensional splines. The target trajectories stored in sparse map  800  may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along a particular road segment. A road segment may be associated with a single target trajectory or multiple target trajectories. For example, on a two lane road, a first target trajectory may be stored to represent an intended path of travel along the road in a first direction, and a second target trajectory may be stored to represent an intended path of travel along the road in another direction (e.g., opposite to the first direction). Additional target trajectories may be stored with respect to a particular road segment. For example, on a multi-lane road one or more target trajectories may be stored representing intended paths of travel for vehicles in one or more lanes associated with the multi-lane road. In some embodiments, each lane of a multi-lane road may be associated with its own target trajectory. In other embodiments, there may be fewer target trajectories stored than lanes present on a multi-lane road. In such cases, a vehicle navigating the multi-lane road may use any of the stored target trajectories to guides its navigation by taking into account an amount of lane offset from a lane for which a target trajectory is stored (e.g., if a vehicle is traveling in the left most lane of a three lane highway, and a target trajectory is stored only for the middle lane of the highway, the vehicle may navigate using the target trajectory of the middle lane by accounting for the amount of lane offset between the middle lane and the left-most lane when generating navigational instructions). 
     In some embodiments, the target trajectory may represent an ideal path that a vehicle should take as the vehicle travels. The target trajectory may be located, for example, at an approximate center of a lane of travel. In other cases, the target trajectory may be located elsewhere relative to a road segment. For example, a target trajectory may approximately coincide with a center of a road, an edge of a road, or an edge of a lane, etc. In such cases, navigation based on the target trajectory may include a determined amount of offset to be maintained relative to the location of the target trajectory. Moreover, in some embodiments, the determined amount of offset to be maintained relative to the location of the target trajectory may differ based on a type of vehicle (e.g., a passenger vehicle including two axles may have a different offset from a truck including more than two axles along at least a portion of the target trajectory). 
     Sparse map  800  may also include data relating to a plurality of predetermined landmarks  820  associated with particular road segments, local maps, etc. As discussed in detail in other sections, these landmarks may be used in navigation of the autonomous vehicle. For example, in some embodiments, the landmarks may be used to determine a current position of the vehicle relative to a stored target trajectory. With this position information, the autonomous vehicle may be able to adjust a heading direction to match a direction of the target trajectory at the determined location. 
     The plurality of landmarks  820  may be identified and stored in sparse map  800  at any suitable spacing. In some embodiments, landmarks may be stored at relatively high densities (e.g., every few meters or more). In some embodiments, however, significantly larger landmark spacing values may be employed. For example, in sparse map  800 , identified (or recognized) landmarks may be spaced apart by 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, the identified landmarks may be located at distances of even more than 2 kilometers apart. Between landmarks, and therefore between determinations of vehicle position relative to a target trajectory, the vehicle may navigate based on dead reckoning in which it uses sensors to determine its ego motion and estimate its position relative to the target trajectory. Because errors may accumulate during navigation by dead reckoning, over time the position determinations relative to the target trajectory may become increasingly less accurate. The vehicle may use landmarks occurring in sparse map  800  (and their known locations) to remove the dead reckoning-induced errors in position determination. In this way, the identified landmarks included in sparse map  800  may serve as navigational anchors from which an accurate position of the vehicle relative to a target trajectory may be determined. Because a certain amount of error may be acceptable in position location, an identified landmark need not always be available to an autonomous vehicle. Rather, suitable navigation may be possible even based on landmark spacings, as noted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1 identified landmark every 1 km of road may be sufficient to maintain a longitudinal position determination accuracy within 1 m. Thus, not every potential landmark appearing along a road segment need be stored in sparse map  800 . 
     In addition to target trajectories and identified landmarks, sparse map  800  may include information relating to various other road features. For example,  FIG. 9A  illustrates a representation of curves along a particular road segment that may be stored in sparse map  800 . In some embodiments, a single lane of a road may be modeled by a three-dimensional polynomial description of left and right sides of the road. Such polynomials representing left and right sides of a single lane are shown in  FIG. 9A . Regardless of how many lanes a road may have, the road may be represented using polynomials in a way similar to that illustrated in  FIG. 9A . For example, left and right sides of a multi-lane road may be represented by polynomials similar to those shown in  FIG. 9A , and intermediate lane markings included on a multi-lane road (e.g., dashed markings representing lane boundaries, solid yellow lines representing boundaries between lanes traveling in different directions, etc.) may also be represented using polynomials such as those shown in  FIG. 9A . 
     As shown in  FIG. 9A , a lane  900  may be represented using polynomials (e.g., a first order, second order, third order, or any suitable order polynomials). For illustration, lane  900  is shown as a two-dimensional lane and the polynomials are shown as two-dimensional polynomials. Lane  900  includes a left side  910  and a right side  920 . In some embodiments, more than one polynomial may be used to represent a location of each side of the road or lane boundary. For example, each of left side  910  and right side  920  may be represented by a plurality of polynomials of any suitable length. In some cases, the polynomials may have a length of about 100 m, although other lengths greater than or less than 100 m may also be used. Additionally, the polynomials can overlap with one another in order to facilitate seamless transitions in navigating based on subsequently encountered polynomials as a host vehicle travels along a roadway. For example, each of left side  910  and right side  920  may be represented by a plurality of third order polynomials separated into segments of about 100 meters in length (an example of the first predetermined range), and overlapping each other by about 50 meters. The polynomials representing the left side  910  and the right side  920  may or may not have the same order. For example, in some embodiments, some polynomials may be second order polynomials, some may be third order polynomials, and some may be fourth order polynomials. 
     In the example shown in  FIG. 9A , left side  910  of lane  900  is represented by two groups of third order polynomials. The first group includes polynomial segments  911 ,  912 , and  913 . The second group includes polynomial segments  914 ,  915 , and  916 . The two groups, while substantially parallel to each other, follow the locations of their respective sides of the road. Polynomial segments  911 - 916  have a length of about 100 meters and overlap adjacent segments in the series by about 50 meters. As noted previously, however, polynomials of different lengths and different overlap amounts may also be used. For example, the polynomials may have lengths of 500 m, 1 km, or more, and the overlap amount may vary from 0 to 50 m, 50 m to 100 m, or greater than 100 m. Additionally, while  FIG. 9A  is shown as representing polynomials extending in 2D space (e.g., on the surface of the paper), it is to be understood that these polynomials may represent curves extending in three dimensions (e.g., including a height component) to represent elevation changes in a road segment in addition to X-Y curvature. 
     Returning to the target trajectories of sparse map  800 ,  FIG. 9B  shows a three-dimensional polynomial representing a target trajectory for a vehicle traveling along a particular road segment. The target trajectory represents not only the X-Y path that a host vehicle should travel along a particular road segment, but also the elevation change that the host vehicle will experience when traveling along the road segment. Thus, each target trajectory in sparse map  800  may be represented by one or more three-dimensional polynomials, like the three-dimensional polynomial  950  shown in  FIG. 9B . Sparse map  800  may include a plurality of trajectories (e.g., millions or billions or more to represent trajectories of vehicles along various road segments along roadways throughout the world). In some embodiments, each target trajectory may correspond to a spline connecting three-dimensional polynomial segments. 
     Regarding the data footprint of polynomial curves stored in sparse map  800 , in some embodiments, each third degree polynomial may be represented by four parameters, each requiring four bytes of data. Suitable representations may be obtained with third degree polynomials requiring about 192 bytes of data for every 100 m. This translates to approximately 200 kB per hour in data usage/transfer requirements for a host vehicle traveling approximately 100 km/hr. 
     Sparse map  800  may describe the lanes network using a combination of geometry descriptors and meta-data. The geometry may be described by polynomials or splines as described above. The meta-data may describe the number of lanes, special characteristics (such as a car pool lane), and possibly other sparse labels. The total footprint of such indicators may be negligible. 
     As previously noted, sparse map  800  may include a plurality of predetermined landmarks associated with a road segment. Rather than storing actual images of the landmarks and relying, for example, on image recognition analysis based on captured images and stored images, each landmark in sparse map  800  may be represented and recognized using less data than a stored, actual image would require. Data representing landmarks may include sufficient information for describing or identifying the landmarks along a road. Storing data describing characteristics of landmarks, rather than the actual images of landmarks, may reduce the size of sparse map  800 . 
       FIG. 10  illustrates examples of types of landmarks that may be represented in sparse map  800 . The landmarks may include any visible and identifiable objects along a road segment. The landmarks may be selected such that they are fixed and do not change often with respect to their locations and/or content. The landmarks included in sparse map  800  may be useful in determining a location of vehicle  200  with respect to a target trajectory as the vehicle traverses a particular road segment. Examples of landmarks may include traffic signs, directional signs, general signs (e.g., rectangular signs), roadside fixtures (e.g., lampposts, reflectors, etc.), and any other suitable category. In some embodiments, lane marks on the road, may also be included as landmarks in sparse map  800 . 
     Examples of landmarks shown in  FIG. 10  include traffic signs, directional signs, roadside fixtures, and general signs. Traffic signs may include, for example, speed limit signs (e.g., speed limit sign  1000 ), yield signs (e.g., yield sign  1005 ), route number signs (e.g., route number sign  1010 ), traffic light signs (e.g., traffic light sign  1015 ), stop signs (e.g., stop sign  1020 ). Directional signs may include a sign that includes one or more arrows indicating one or more directions to different places. For example, directional signs may include a highway sign  1025  having arrows for directing vehicles to different roads or places, an exit sign  1030  having an arrow directing vehicles off a road, etc. 
     General signs may be unrelated to traffic. For example, general signs may include billboards used for advertisement, or a welcome board adjacent a border between two countries, states, counties, cities, or towns.  FIG. 10  shows a general sign  1040  (“Joe&#39;s Restaurant”). Although general sign  1040  may have a rectangular shape, as shown in  FIG. 10 , general sign  1040  may have other shapes, such as square, circle, triangle, etc. 
     Landmarks may also include roadside fixtures. Roadside fixtures may be objects that are not signs, and may not be related to traffic or directions. For example, roadside fixtures may include lampposts (e.g., lamppost  1035 ), power line posts, traffic light posts, etc. 
     Landmarks may also include beacons that may be specifically designed for usage in an autonomous vehicle navigation system. For example, such beacons may include stand-alone structures placed at predetermined intervals to aid in navigating a host vehicle. Such beacons may also include visual/graphical information added to existing road signs (e.g., icons, emblems, bar codes, etc.) that may be identified or recognized by a vehicle traveling along a road segment. Such beacons may also include electronic components. In such embodiments, electronic beacons (e.g., RFID tags, etc.) may be used to transmit non-visual information to a host vehicle. Such information may include, for example, landmark identification and/or landmark location information that a host vehicle may use in determining its position along a target trajectory. 
     In some embodiments, the landmarks included in sparse map  800  may be represented by a data object of a predetermined size. The data representing a landmark may include any suitable parameters for identifying a particular landmark. For example, in some embodiments, landmarks stored in sparse map  800  may include parameters such as a physical size of the landmark (e.g., to support estimation of distance to the landmark based on a known size/scale), a distance to a previous landmark, lateral offset, height, a type code (e.g., a landmark type—what type of directional sign, traffic sign, etc.), a GPS coordinate (e.g., to support global localization), and any other suitable parameters. Each parameter may be associated with a data size. For example, a landmark size may be stored using 8 bytes of data. A distance to a previous landmark, a lateral offset, and height may be specified using 12 bytes of data. A type code associated with a landmark such as a directional sign or a traffic sign may require about 2 bytes of data. For general signs, an image signature enabling identification of the general sign may be stored using 50 bytes of data storage. The landmark GPS position may be associated with 16 bytes of data storage. These data sizes for each parameter are examples only, and other data sizes may also be used. 
     Representing landmarks in sparse map  800  in this manner may offer a lean solution for efficiently representing landmarks in the database. In some embodiments, signs may be referred to as semantic signs and non-semantic signs. A semantic sign may include any class of signs for which there&#39;s a standardized meaning (e.g., speed limit signs, warning signs, directional signs, etc.). A non-semantic sign may include any sign that is not associated with a standardized meaning (e.g., general advertising signs, signs identifying business establishments, etc.). For example, each semantic sign may be represented with 38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance to previous landmark, lateral offset, and height; 2 bytes for a type code; and 16 bytes for GPS coordinates). Sparse map  800  may use a tag system to represent landmark types. In some cases, each traffic sign or directional sign may be associated with its own tag, which may be stored in the database as part of the landmark identification. For example, the database may include on the order of 1000 different tags to represent various traffic signs and on the order of about 10000 different tags to represent directional signs. Of course, any suitable number of tags may be used, and additional tags may be created as needed. General purpose signs may be represented in some embodiments using less than about 100 bytes (e.g., about 86 bytes including 8 bytes for size; 12 bytes for distance to previous landmark, lateral offset, and height; 50 bytes for an image signature; and 16 bytes for GPS coordinates). 
     Thus, for semantic road signs not requiring an image signature, the data density impact to sparse map  800 , even at relatively high landmark densities of about 1 per 50 m, may be on the order of about 760 bytes per kilometer (e.g., 20 landmarks per km×38 bytes per landmark=760 bytes). Even for general purpose signs including an image signature component, the data density impact is about 1.72 kB per km (e.g., 20 landmarks per km×86 bytes per landmark=1,720 bytes). For semantic road signs, this equates to about 76 kB per hour of data usage for a vehicle traveling 100 km/hr. For general purpose signs, this equates to about 170 kB per hour for a vehicle traveling 100 km/hr. 
     In some embodiments, a generally rectangular object, such as a rectangular sign, may be represented in sparse map  800  by no more than 100 byte of data. The representation of the generally rectangular object (e.g., general sign  1040 ) in sparse map  800  may include a condensed image signature (e.g., condensed image signature  1045 ) associated with the generally rectangular object. This condensed image signature may be used, for example, to aid in identification of a general purpose sign, for example, as a recognized landmark. Such a condensed image signature (e.g., image information derived from actual image data representing an object) may avoid a need for storage of an actual image of an object or a need for comparative image analysis performed on actual images in order to recognize landmarks. 
     Referring to  FIG. 10 , sparse map  800  may include or store a condensed image signature  1045  associated with a general sign  1040 , rather than an actual image of general sign  1040 . For example, after an image capture device (e.g., image capture device  122 ,  124 , or  126 ) captures an image of general sign  1040 , a processor (e.g., image processor  190  or any other processor that can process images either aboard or remotely located relative to a host vehicle) may perform an image analysis to extract/create condensed image signature  1045  that includes a unique signature or pattern associated with general sign  1040 . In one embodiment, condensed image signature  1045  may include a shape, color pattern, a brightness pattern, or any other feature that may be extracted from the image of general sign  1040  for describing general sign  1040 . For example, in  FIG. 10 , the circles, triangles, and stars shown in condensed image signature  1045  may represent areas of different colors. The pattern represented by the circles, triangles, and stars may be stored in sparse map  800 , e.g., within the 50 bytes designated to include an image signature. Notably, the circles, triangles, and stars are not necessarily meant to indicate that such shapes are stored as part of the image signature. Rather, these shapes are meant to conceptually represent recognizable areas having discernible color differences, textual areas, graphical shapes, or other variations in characteristics that may be associated with a general purpose sign. Such condensed image signatures can be used to identify a landmark in the form of a general sign. For example, the condensed image signature can be used to perform a same-not-same analysis based on a comparison of a stored condensed image signature with image data captured, for example, using a camera onboard an autonomous vehicle. 
     Returning to the target trajectories a host vehicle may use to navigate a particular road segment,  FIG. 11A  shows polynomial representations trajectories capturing during a process of building or maintaining sparse map  800 . A polynomial representation of a target trajectory included in sparse map  800  may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in sparse map  800  may be an aggregation of two or more reconstructed trajectories of prior traversals of vehicles along the same road segment. In some embodiments, the polynomial representation of the target trajectory included in sparse map  800  may be an average of the two or more reconstructed trajectories of prior traversals of vehicles along the same road segment. Other mathematical operations may also be used to construct a target trajectory along a road path based on reconstructed trajectories collected from vehicles traversing along a road segment. 
     As shown in  FIG. 11A , a road segment  1100  may be traveled by a number of vehicles  200  at different times. Each vehicle  200  may collect data relating to a path that it took along the road segment. The path traveled by a particular vehicle may be determined based on camera data, accelerometer information, speed sensor information, and/or GPS information, among other potential sources. Such data may be used to reconstruct trajectories of vehicles traveling along the road segment, and based on these reconstructed trajectories, a target trajectory (or multiple target trajectories) may be determined for the particular road segment. Such target trajectories may represent a preferred path of a host vehicle (e.g., guided by an autonomous navigation system) as it travels along the road segment. 
     In the example shown in  FIG. 11A , a first reconstructed trajectory  1101  may be determined based on data received from a first vehicle traversing road segment  1100  at a first time period (e.g., day  1 ), a second reconstructed trajectory  1102  may be obtained from a second vehicle traversing road segment  1100  at a second time period (e.g., day  2 ), and a third reconstructed trajectory  1103  may be obtained from a third vehicle traversing road segment  1100  at a third time period (e.g., day  3 ). Each trajectory  1101 ,  1102 , and  1103  may be represented by a polynomial, such as a three-dimensional polynomial. It should be noted that in some embodiments, any of the reconstructed trajectories may be assembled onboard the vehicles traversing road segment  1100 . 
     Additionally, or alternatively, such reconstructed trajectories may be determined on a server side based on information received from vehicles traversing road segment  1100 . For example, in some embodiments, vehicles  200  may transmit data to one or more servers relating to their motion along road segment  1100  (e.g., steering angle, heading, time, position, speed, sensed road geometry, and/or sensed landmarks, among things). The server may reconstruct trajectories for vehicles  200  based on the received data. The server may also generate a target trajectory for guiding navigation of autonomous vehicle that will travel along the same road segment  1100  at a later time based on the first, second, and third trajectories  1101 ,  1102 , and  1103 . While a target trajectory may be associated with a single prior traversal of a road segment, in some embodiments, each target trajectory included in sparse map  800  may be determined based on two or more reconstructed trajectories of vehicles traversing the same road segment. In  FIG. 11A , the target trajectory is represented by  1110 . In some embodiments, the target trajectory  1110  may be generated based on an average of the first, second, and third trajectories  1101 ,  1102 , and  1103 . In some embodiments, the target trajectory  1110  included in sparse map  800  may be an aggregation (e.g., a weighted combination) of two or more reconstructed trajectories. 
       FIGS. 11B and 11C  further illustrate the concept of target trajectories associated with road segments present within a geographic region  1111 . As shown in  FIG. 11B , a first road segment  1120  within geographic region  1111  may include a multilane road, which includes two lanes  1122  designated for vehicle travel in a first direction and two additional lanes  1124  designated for vehicle travel in a second direction opposite to the first direction. Lanes  1122  and lanes  1124  may be separated by a double yellow line  1123 . Geographic region  1111  may also include a branching road segment  1130  that intersects with road segment  1120 . Road segment  1130  may include a two-lane road, each lane being designated for a different direction of travel. Geographic region  1111  may also include other road features, such as a stop line  1132 , a stop sign  1134 , a speed limit sign  1136 , and a hazard sign  1138 . 
     As shown in  FIG. 11C , sparse map  800  may include a local map  1140  including a road model for assisting with autonomous navigation of vehicles within geographic region  1111 . For example, local map  1140  may include target trajectories for one or more lanes associated with road segments  1120  and/or  1130  within geographic region  1111 . For example, local map  1140  may include target trajectories  1141  and/or  1142  that an autonomous vehicle may access or rely upon when traversing lanes  1122 . Similarly, local map  1140  may include target trajectories  1143  and/or  1144  that an autonomous vehicle may access or rely upon when traversing lanes  1124 . Further, local map  1140  may include target trajectories  1145  and/or  1146  that an autonomous vehicle may access or rely upon when traversing road segment  1130 . Target trajectory  1147  represents a preferred path an autonomous vehicle should follow when transitioning from lanes  1120  (and specifically, relative to target trajectory  1141  associated with a right-most lane of lanes  1120 ) to road segment  1130  (and specifically, relative to a target trajectory  1145  associated with a first side of road segment  1130 . Similarly, target trajectory  1148  represents a preferred path an autonomous vehicle should follow when transitioning from road segment  1130  (and specifically, relative to target trajectory  1146 ) to a portion of road segment  1124  (and specifically, as shown, relative to a target trajectory  1143  associated with a left lane of lanes  1124 . 
     Sparse map  800  may also include representations of other road-related features associated with geographic region  1111 . For example, sparse map  800  may also include representations of one or more landmarks identified in geographic region  1111 . Such landmarks may include a first landmark  1150  associated with stop line  1132 , a second landmark  1152  associated with stop sign  1134 , a third landmark associated with speed limit sign  1154 , and a fourth landmark  1156  associated with hazard sign  1138 . Such landmarks may be used, for example, to assist an autonomous vehicle in determining its current location relative to any of the shown target trajectories, such that the vehicle may adjust its heading to match a direction of the target trajectory at the determined location. 
     In some embodiments, sparse may  800  may also include road signature profiles. Such road signature profiles may be associated with any discernible/measurable variation in at least one parameter associated with a road. For example, in some cases, such profiles may be associated with variations in surface roughness of a particular road segment, variations in road width over a particular road segment, variations in distances between dashed lines painted along a particular road segment, variations in road curvature along a particular road segment, etc.  FIG. 11D  shows an example of a road signature profile  1160 . While profile  1160  may represent any of the parameters mentioned above, or others, in one example, profile  1160  may represent a measure of road surface roughness, as obtained, for example, by monitoring one or more sensors providing outputs indicative of an amount of suspension displacement as a vehicle travels a particular road segment. Alternatively, profile  1160  may represent variation in road width, as determined based on image data obtained via a camera onboard a vehicle traveling a particular road segment. Such profiles may be useful, for example, in determining a particular location of an autonomous vehicle relative to a particular target trajectory. That is, as it traverses a road segment, an autonomous vehicle may measure a profile associated with one or more parameters associated with the road segment. If the measured profile can be correlated/matched with a predetermined profile that plots the parameter variation with respect to position along the road segment, then the measured and predetermined profiles may be used (e.g., by overlaying corresponding sections of the measured and predetermined profiles) in order to determine a current position along the road segment and, therefore, a current position relative to a target trajectory for the road segment. 
     In some embodiments, sparse map  800  may include different trajectories based on different characteristics associated with a user of autonomous vehicles, environmental conditions, and/or other parameters relating to driving. For example, in some embodiments, different trajectories may be generated based on different user preferences and/or profiles. Sparse map  800  including such different trajectories may be provided to different autonomous vehicles of different users. For example, some users may prefer to avoid toll roads, while others may prefer to take the shortest or fastest routes, regardless of whether there is a toll road on the route. The disclosed systems may generate different sparse maps with different trajectories based on such different user preferences or profiles. As another example, some users may prefer to travel in a fast moving lane, while others may prefer to maintain a position in the central lane at all times. 
     Different trajectories may be generated and included in sparse map  800  based on different environmental conditions, such as day and night, snow, rain, fog, etc. Autonomous vehicles driving under different environmental conditions may be provided with sparse map  800  generated based on such different environmental conditions. In some embodiments, cameras provided on autonomous vehicles may detect the environmental conditions, and may provide such information back to a server that generates and provides sparse maps. For example, the server may generate or update an already generated sparse map  800  to include trajectories that may be more suitable or safer for autonomous driving under the detected environmental conditions. The update of sparse map  800  based on environmental conditions may be performed dynamically as the autonomous vehicles are traveling along roads. 
     Other different parameters relating to driving may also be used as a basis for generating and providing different sparse maps to different autonomous vehicles. For example, when an autonomous vehicle is traveling at a high speed, turns may be tighter. Trajectories associated with specific lanes, rather than roads, may be included in sparse map  800  such that the autonomous vehicle may maintain within a specific lane as it follows a specific trajectory. When an image captured by a camera onboard the autonomous vehicle indicates that the vehicle has drifted outside of the lane (e.g., crossed the lane mark), an action may be triggered within the vehicle to bring the vehicle back to the designated lane according to the specific trajectory. 
     Constructing a Road Model for Autonomous Vehicle Navigation 
     In some embodiments, the disclosed systems and methods may construct a road model for autonomous vehicle navigation. For example, the road model may include crowd sourced data. The disclosed systems and methods may refine the crowd sourced data based on observed local conditions. Further, the disclosed systems and methods may determine a refined trajectory for an autonomous vehicle based on sensor information. Still further, the disclosed systems and methods may identify landmarks for use in the road model, as well refine the positions of the landmarks in the road model. These systems and methods are disclosed in further detail in the following sections. 
     Crowd Sourcing Data for Autonomous Vehicle Navigation 
     In some embodiments, the disclosed systems and methods may construct a road model for autonomous vehicle navigation. For example, disclosed systems and methods may use crowd sourced data for generation of an autonomous vehicle road model that one or more autonomous vehicles may use to navigate along a system of roads. By crowd sourcing, it means that data are received from various vehicles (e.g., autonomous vehicles) traveling on a road segment at different times and such data are used to generate and/or update the road model. The model may, in turn, be transmitted to the vehicles or other vehicles later traveling along the road segment for assisting autonomous vehicle navigation. The road model may include a plurality of target trajectories representing preferred trajectories that autonomous vehicles should follow as they traverse a road segment. The target trajectories may be the same as a reconstructed actual trajectory collected from a vehicle traversing a road segment, which may be transmitted from the vehicle to a server. In some embodiments, the target trajectories may be different from actual trajectories that one or more vehicles previously took when traversing a road segment. The target trajectories may be generated based on actual trajectories (e.g., through averaging or any other suitable operation). 
     The vehicle trajectory data that a vehicle may upload to a server may correspond with the actual reconstructed trajectory for the vehicle, or it may correspond to a recommended trajectory, which may be based on or related to the actual reconstructed trajectory of the vehicle, but may differ from the actual reconstructed trajectory. For example, vehicles may modify their actual, reconstructed trajectories and submit (e.g., recommend) to the server the modified actual trajectories. The road model may use the recommended, modified trajectories as target trajectories for autonomous navigation of other vehicles. 
     In addition to trajectory information, other information for potential use in building a sparse data map  800  may include information relating to potential landmark candidates. For example, through crowd sourcing of information, the disclosed systems and methods may identify potential landmarks in an environment and refine landmark positions. The landmarks may be used by a navigation system of autonomous vehicles to determine and/or adjust the position of the vehicle along the target trajectories. 
     The reconstructed trajectories that a vehicle may generate as it travels along a road may be obtained by any suitable method. In some embodiments, the reconstructed trajectories may be developed by stitching together segments of motion for the vehicle, using, e.g., ego motion estimation (e.g., three dimensional translation and three dimensional rotation of the camera, and hence the body of the vehicle). The rotation and translation estimation may be determined based on analysis of images captured by one or more image capture devices along with information from other sensors or devices, such as inertial sensors and speed sensors. For example, the inertial sensors may include an accelerometer or other suitable sensors configured to measure changes in translation and/or rotation of the vehicle body. The vehicle may include a speed sensor that measures a speed of the vehicle. 
     In some embodiments, the ego motion of the camera (and hence the vehicle body) may be estimated based on an optical flow analysis of the captured images. An optical flow analysis of a sequence of images identifies movement of pixels from the sequence of images, and based on the identified movement, determines motions of the vehicle. The ego motion may be integrated over time and along the road segment to reconstruct a trajectory associated with the road segment that the vehicle has followed. 
     Data (e.g., reconstructed trajectories) collected by multiple vehicles in multiple drives along a road segment at different times may be used to construct the road model (e.g., including the target trajectories, etc.) included in sparse data map  800 . Data collected by multiple vehicles in multiple drives along a road segment at different times may also be averaged to increase an accuracy of the model. In some embodiments, data regarding the road geometry and/or landmarks may be received from multiple vehicles that travel through the common road segment at different times. Such data received from different vehicles may be combined to generate the road model and/or to update the road model. 
     The disclosed systems and methods may enable autonomous vehicle navigation (e.g., steering control) with low footprint models, which may be collected by the autonomous vehicles themselves without the aid of expensive surveying equipment. To support the autonomous navigation (e.g., steering applications), the road model may include the geometry of the road, its lane structure, and landmarks that may be used to determine the location or position of vehicles along a trajectory included in the model. Generation of the road model may be performed by a remote server that communicates with vehicles traveling on the road and that receives data from the vehicles. The data may include sensed data, trajectories reconstructed based on the sensed data, and/or recommended trajectories that may represent modified reconstructed trajectories. The server may transmit the model back to the vehicles or other vehicles that later travel on the road to aid in autonomous navigation. 
     The geometry of a reconstructed trajectory (and also a target trajectory) along a road segment may be represented by a curve in three dimensional space, which may be a spline connecting three dimensional polynomials. The reconstructed trajectory curve may be determined from analysis of a video stream or a plurality of images captured by a camera installed on the vehicle. In some embodiments, a location is identified in each frame or image that is a few meters ahead of the current position of the vehicle. This location is where the vehicle is expected to travel to in a predetermined time period. This operation may be repeated frame by frame, and at the same time, the vehicle may compute the camera&#39;s ego motion (rotation and translation). At each frame or image, a short range model for the desired path is generated by the vehicle in a reference frame that is attached to the camera. The short range models may be stitched together to obtain a three dimensional model of the road in some coordinate frame, which may be an arbitrary or predetermined coordinate frame. The three dimensional model of the road may then be fitted by a spline, which may include or connect one or more polynomials of suitable orders. 
     To conclude the short range road model at each frame, one or more detection modules may be used. For example, a bottom-up lane detection module may be used. The bottom-up lane detection module may be useful when lane marks are drawn on the road. This module may look for edges in the image and assembles them together to form the lane marks. A second module may be used together with the bottom-up lane detection module. The second module is an end-to-end deep neural network, which may be trained to predict the correct short range path from an input image. In both modules, the road model may be detected in the image coordinate frame and transformed to a three dimensional space that may be virtually attached to the camera. 
     Although the reconstructed trajectory modeling method may introduce an accumulation of errors due to the integration of ego motion over a long period of time, which may include a noise component, such errors may be inconsequential as the generated model may provide sufficient accuracy for navigation over a local scale. In addition, it is possible to cancel the integrated error by using external sources of information, such as satellite images or geodetic measurements. For example, the disclosed systems and methods may use a GNSS receiver to cancel accumulated errors. However, the GNSS positioning signals may not be always available and accurate. The disclosed systems and methods may enable a steering application that depends weakly on the availability and accuracy of GNSS positioning. In such systems, the usage of the GNSS signals may be limited. For example, in some embodiments, the disclosed systems may use the GNSS signals for database indexing purposes only. 
     In some embodiments, the range scale (e.g., local scale) that may be relevant for an autonomous vehicle navigation steering application may be on the order of 50 meters, 100 meters, 200 meters, 300 meters, etc. Such distances may be used, as the geometrical road model is mainly used for two purposes: planning the trajectory ahead and localizing the vehicle on the road model. In some embodiments, the planning task may use the model over a typical range of 40 meters ahead (or any other suitable distance ahead, such as 20 meters, 30 meters, 50 meters), when the control algorithm steers the vehicle according to a target point located 1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7 seconds, 2 seconds, etc.). The localization task uses the road model over a typical range of 60 meters behind the car (or any other suitable distances, such as 50 meters, 100 meters, 150 meters, etc.), according to a method called “tail alignment” described in more detail in another section. The disclosed systems and methods may generate a geometrical model that has sufficient accuracy over particular range, such as 100 meters, such that a planned trajectory will not deviate by more than, for example, 30 cm from the lane center. 
     As explained above, a three dimensional road model may be constructed from detecting short range sections and stitching them together. The stitching may be enabled by computing a six degree ego motion model, using the videos and/or images captured by the camera, data from the inertial sensors that reflect the motions of the vehicle, and the host vehicle velocity signal. The accumulated error may be small enough over some local range scale, such as of the order of 100 meters. All this may be completed in a single drive over a particular road segment. 
     In some embodiments, multiple drives may be used to average the resulted model, and to increase its accuracy further. The same car may travel the same route multiple times, or multiple cars may send their collected model data to a central server. In any case, a matching procedure may be performed to identify overlapping models and to enable averaging in order to generate target trajectories. The constructed model (e.g., including the target trajectories) may be used for steering once a convergence criterion is met. Subsequent drives may be used for further model improvements and in order to accommodate infrastructure changes. 
     Sharing of driving experience (such as sensed data) between multiple cars becomes feasible if they are connected to a central server. Each vehicle client may store a partial copy of a universal road model, which may be relevant for its current position. A bidirectional update procedure between the vehicles and the server may be performed by the vehicles and the server. The small footprint concept discussed above enables the disclosed systems and methods to perform the bidirectional updates using a very small bandwidth. 
     Information relating to potential landmarks may also be determined and forwarded to a central server. For example, the disclosed systems and methods may determine one or more physical properties of a potential landmark based on one or more images that include the landmark. The physical properties may include a physical size (e.g., height, width) of the landmark, a distance from a vehicle to a landmark, a distance between the landmark to a previous landmark, the lateral position of the landmark (e.g., the position of the landmark relative to the lane of travel), the GPS coordinates of the landmark, a type of landmark, identification of text on the landmark, etc. For example, a vehicle may analyze one or more images captured by a camera to detect a potential landmark, such as a speed limit sign. The vehicle may determine a distance from the vehicle to the landmark based on the analysis of the one or more images. In some embodiments, the distance may be determined based on analysis of images of the landmark using a suitable image analysis method, such as a scaling method and/or an optical flow method. In some embodiments, the disclosed systems and methods may be configured to determine a type or classification of a potential landmark. In case the vehicle determines that a certain potential landmark corresponds to a predetermined type or classification stored in a sparse map, it may be sufficient for the vehicle to communicate to the server an indication of the type or classification of the landmark, along with its location. The server may store such indications. At a later time, other vehicles may capture an image of the landmark, process the image (e.g., using a classifier), and compare the result from processing the image to the indication stored in the server with regard to the type of landmark. There may be various types of landmarks, and different types of landmarks may be associated with different types of data to be uploaded to and stored in the server, different processing onboard the vehicle may detects the landmark and communicate information about the landmark to the server, and the system onboard the vehicle may receive the landmark data from the server and use the landmark data for identifying a landmark in autonomous navigation. 
     In some embodiments, multiple autonomous vehicles traveling on a road segment may communicate with a server. The vehicles (or clients) may generate a curve describing its drive (e.g., through ego motion integration) in an arbitrary coordinate frame. The vehicles may detect landmarks and locate them in the same frame. The vehicles may upload the curve and the landmarks to the server. The server may collect data from vehicles over multiple drives, and generate a unified road model. The server may distribute the model to clients (e.g., vehicles). The server may continuously or periodically update the model when receiving new data from the vehicles. For example, the server may process the new data to evaluate whether it includes information that should trigger an updated, or creation of new data on the server. The server may distribute the updated model or the updates to the vehicles for providing autonomous vehicle navigation. 
     The server may use one or more criteria for determining whether new data received from the vehicles should trigger an update to the model or trigger creation of new data. For example, when the new data indicates that a previously recognized landmark at a specific location no longer exists, or is replaced by another landmark, the server may determine that the new data should trigger an update to the model. As another example, when the new data indicates that a road segment has been closed, and when this has been corroborated by data received from other vehicles, the server may determine that the new data should trigger an update to the model. 
     The server may distribute the updated model (or the updated portion of the model) to one or more vehicles that are traveling on the road segment, with which the updates to the model are associated. The server may also distribute the updated model to vehicles that are about to travel on the road segment, or vehicles whose planned trip includes the road segment, with which the updates to the model are associated. For example, while an autonomous vehicle is traveling along another road segment before reaching the road segment with which an update is associated, the server may distribute the updates or updated model to the autonomous vehicle before it reaches the road segment. 
     In some embodiments, the remote server may collect trajectories and landmarks from multiple clients (e.g., vehicles that travel along a common road segment). The server may match curves using landmarks and create an average road model based on the trajectories collected from the multiple vehicles. The server may also compute a graph of roads and the most probable path at each node or conjunction of the road segment. 
     The server may average landmark properties received from multiple vehicles that traveled along the common road segment, such as the distances between one landmark to another (e.g., a previous one along the road segment) as measured by multiple vehicles, to determine an arc-length parameter and support localization along the path and speed calibration for each client vehicle. The server may average the physical dimensions of a landmark measured by multiple vehicles traveled along the common road segment and recognized the same landmark. The averaged physical dimensions may be used to support distance estimation, such as the distance from the vehicle to the landmark. The server may average lateral positions of a landmark (e.g., position from the lane in which vehicles are traveling in to the landmark) measured by multiple vehicles traveled along the common road segment and recognized the same landmark. The averaged lateral potion may be used to support lane assignment. The server may average the GPS coordinates of the landmark measured by multiple vehicles traveled along the same road segment and recognized the same landmark. The averaged GPS coordinates of the landmark may be used to support global localization or positioning of the landmark in the road model. 
     In some embodiments, the server may identify model changes, such as constructions, detours, new signs, removal of signs, etc., based on data received from the vehicles. The server may continuously or periodically or instantaneously update the model upon receiving new data from the vehicles. The server may distribute updates to the model or the updated model to vehicles for providing autonomous navigation. 
     In some embodiments, the server may analyze driver interventions during the autonomous driving. The server may analyze data received from the vehicle at the time and location where intervention occurs, and/or data received prior to the time the intervention occurred. The server may identify certain portions of the data that caused or are closely related to the intervention, for example, data indicating a temporary lane closure setup, data indicating a pedestrian in the road. The server may update the model based on the identified data. For example, the server may modify one or more trajectories stored in the model. 
     Consistent with disclosed embodiments, the system can store information obtained during autonomous navigation (or regular driver-controlled navigation) for use in later traversals along the same road. The system may share that information with other vehicles when they navigate along the road. Each client system may then further refine the crowd sourced data based on observed local conditions. 
       FIG. 12  is a schematic illustration of a system that uses crowd sourcing data for autonomous vehicle navigation.  FIG. 12  shows a road segment  1200  that includes one or more lanes. A plurality of vehicles  1205 ,  1210 ,  1215 ,  1220 , and  1225  may travel on road segment  1200  at the same time or at different times (although shown as appearing on road segment  1200  at the same time in  FIG. 12 ). At least one of vehicles  1205 - 1225  may be an autonomous vehicle. For simplicity of the present example, all of the vehicles  1205 - 1225  are presumed to be autonomous vehicles. Each vehicle may be similar to vehicles disclosed in other embodiments (e.g., vehicle  200 ), and may include components or devices included in or associated with vehicles disclosed in other embodiments. Each vehicle may be equipped with an image capture device or camera (e.g., image capture device  122  or camera  122 ). Each vehicle may communicate with a remote server  1230  via one or more networks (e.g., over a cellular network and/or the Internet, etc.) through wireless communication paths  1235 , as indicated by the dashed lines. Each vehicle may transmit data to server  1230  and receive data from server  1230 . For example, server  1230  may collect data from multiple vehicles traveling on the road segment  1200  at different times, and may process the collected data to generate an autonomous vehicle road navigation model, or an update to the model. Server  1230  may transmit the autonomous vehicle road navigation model or the update to the model to the vehicles that transmitted data to server  1230 . Server  1230  may transmit the autonomous vehicle road navigation model or the update to the model to other vehicles that travel on road segment  1200  at later times. 
     As vehicles  1205 - 1225  travel on road segment  1200 , navigation information collected (e.g., detected, sensed, or measured) by vehicles  1205 - 1225  may be transmitted to server  1230 . In some embodiments, the navigation information may be associated with the common road segment  1200 . The navigation information may include a trajectory associated with each of the vehicles  1205 - 1225  as each vehicle travels over road segment  1200 . In some embodiments, the trajectory may be reconstructed based on data sensed by various sensors and devices provided on vehicle  1205 . For example, the trajectory may be reconstructed based on at least one of accelerometer data, speed data, landmarks data, road geometry or profile data, vehicle positioning data, and ego motion data. In some embodiments, the trajectory may be reconstructed based on data from inertial sensors, such as accelerometer, and the velocity of vehicle  1205  sensed by a speed sensor. In addition, in some embodiments, the trajectory may be determined (e.g., by a processor onboard each of vehicles  1205 - 1225 ) based on sensed ego motion of the camera, which may indicate three dimensional translation and/or three dimensional rotations (or rotational motions). The ego motion of the camera (and hence the vehicle body) may be determined from analysis of one or more images captured by the camera. 
     In some embodiments, the trajectory of vehicle  1205  may be determined by a processor provided aboard vehicle  1205  and transmitted to server  1230 . In other embodiments, server  1230  may receive data sensed by the various sensors and devices provided in vehicle  1205 , and determine the trajectory based on the data received from vehicle  1205 . 
     In some embodiments, the navigation information transmitted from vehicles  1205 - 1225  to server  1230  may include data regarding the road geometry or profile. The geometry of road segment  1200  may include lane structure and/or landmarks. The lane structure may include the total number of lanes of road segment  1200 , the type of lanes (e.g., one-way lane, two-way lane, driving lane, passing lane, etc.), markings on lanes, width of lanes, etc. In some embodiments, the navigation information may include a lane assignment, e.g., which lane of a plurality of lanes a vehicle is traveling in. For example, the lane assignment may be associated with a numerical value “3” indicating that the vehicle is traveling on the third lane from the left or right. As another example, the lane assignment may be associated with a text value “center lane” indicating the vehicle is traveling on the center lane. 
     Server  1230  may store the navigation information on a non-transitory computer-readable medium, such as a hard drive, a compact disc, a tape, a memory, etc. Server  1230  may generate (e.g., through a processor included in server  1230 ) at least a portion of an autonomous vehicle road navigation model for the common road segment  1200  based on the navigation information received from the plurality of vehicles  1205 - 1225 . Server  1230  may determine a trajectory associated with each lane based on crowd sourced data (e.g., navigation information) received from multiple vehicles (e.g.,  1205 - 1225 ) that travel on a lane of road segment at different times. Server  1230  may generate the autonomous vehicle road navigation model or a portion of the model (e.g., an updated portion) based on a plurality of trajectories determined based on the crowd sourced navigation data. Server  1230  may transmit the model or the updated portion of the model to one or more of autonomous vehicles  1205 - 1225  traveling on road segment  1200  or any other autonomous vehicles that travel on road segment at a later time for updating an existing autonomous vehicle road navigation model provided in a navigation system of the vehicles. The autonomous vehicle road navigation model may be used by the autonomous vehicles in autonomously navigating along the common road segment  1200 . 
     In some embodiments, the autonomous vehicle road navigation model may be included in a sparse map (e.g., sparse map  800  depicted in  FIG. 8 ). Sparse map  800  may include sparse recording of data related to road geometry and/or landmarks along a road, which may provide sufficient information for guiding autonomous navigation of an autonomous vehicle, yet does not require excessive data storage. In some embodiments, the autonomous vehicle road navigation model may be stored separately from sparse map  800 , and may use map data from sparse map  800  when the model is executed for navigation. In some embodiments, the autonomous vehicle road navigation model may use map data included in sparse map  800  for determining target trajectories along road segment  1200  for guiding autonomous navigation of autonomous vehicles  1205 - 1225  or other vehicles that later travel along road segment  1200 . For example, when the autonomous vehicle road navigation model is executed by a processor included in a navigation system of vehicle  1205 , the model may cause the processor to compare the trajectories determined based on the navigation information received from vehicle  1205  with predetermined trajectories included in sparse map  800  to validate and/or correct the current traveling course of vehicle  1205 . 
     In the autonomous vehicle road navigation model, the geometry of a road feature or target trajectory may be encoded by a curve in a three-dimensional space. In one embodiment, the curve may be a three dimensional spline including one or more connecting three dimensional polynomials. As one of skill in the art would understand, a spline may be a numerical function that is piece-wise defined by a series of polynomials for fitting data. A spline for fitting the three dimensional geometry data of the road may include a linear spline (first order), a quadratic spline (second order), a cubic spline (third order), or any other splines (other orders), or a combination thereof. The spline may include one or more three dimensional polynomials of different orders connecting (e.g., fitting) data points of the three dimensional geometry data of the road. In some embodiments, the autonomous vehicle road navigation model may include a three dimensional spline corresponding to a target trajectory along a common road segment (e.g., road segment  1200 ) or a lane of the road segment  1200 . 
     The autonomous vehicle road navigation model may include other information, such as identification of at least one landmark along road segment  1200 . The landmark may be visible within a field of view of a camera (e.g., camera  122 ) installed on each of vehicles  1205 - 1225 . In some embodiments, camera  122  may capture an image of a landmark. A processor (e.g., processor  180 ,  190 , or processing unit  110 ) provided on vehicle  1205  may process the image of the landmark to extract identification information for the landmark. The landmark identification information, rather than an actual image of the landmark, may be stored in sparse map  800 . The landmark identification information may require much less storage space than an actual image. Other sensors or systems (e.g., GPS system) may also provide certain identification information of the landmark (e.g., position of landmark). The landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign (e.g., a highway exit sign with an arrow indicating a direction, a highway sign with arrows pointing to different directions or places), a landmark beacon, or a lamppost. A landmark beacon refers to a device (e.g., an RFID device) installed along a road segment that transmits or reflects a signal to a receiver installed on a vehicle, such that when the vehicle passes by the device, the beacon received by the vehicle and the location of the device (e.g., determined from GPS location of the device) may be used as a landmark to be included in the autonomous vehicle road navigation model and/or the sparse map  800 . 
     The identification of at least one landmark may include a position of the at least one landmark. The position of the landmark may be determined based on position measurements performed using sensor systems (e.g., Global Positioning Systems, inertial based positioning systems, landmark beacon, etc.) associated with the plurality of vehicles  1205 - 1225 . In some embodiments, the position of the landmark may be determined by averaging the position measurements detected, collected, or received by sensor systems on different vehicles  1205 - 1225  through multiple drives. For example, vehicles  1205 - 1225  may transmit position measurements data to server  1230 , which may average the position measurements and use the averaged position measurement as the position of the landmark. The position of the landmark may be continuously refined by measurements received from vehicles in subsequent drives. 
     The identification of the landmark may include a size of the landmark. The processor provided on a vehicle (e.g.,  1205 ) may estimate the physical size of the landmark based on the analysis of the images. Server  1230  may receive multiple estimates of the physical size of the same landmark from different vehicles over different drives. Server  1230  may average the different estimates to arrive at a physical size for the landmark, and store that landmark size in the road model. The physical size estimate may be used to further determine or estimate a distance from the vehicle to the landmark. The distance to the landmark may be estimated based on the current speed of the vehicle and a scale of expansion based on the position of the landmark appearing in the images relative to the focus of expansion of the camera. For example, the distance to landmark may be estimated by Z=V*dt*R/D, where V is the speed of vehicle, R is the distance in the image from the landmark at time t 1  to the focus of expansion, and D is the change in distance for the landmark in the image from t 1  to t 2 . dt represents the (t 2 −t 1 ). For example, the distance to landmark may be estimated by Z=V*dt*R/D, where V is the speed of vehicle, R is the distance in the image between the landmark and the focus of expansion, dt is a time interval, and D is the image displacement of the landmark along the epipolar line. Other equations equivalent to the above equation, such as Z=V*ω/Δω, may be used for estimating the distance to the landmark. Here, V is the vehicle speed, ω is an image length (like the object width), and Δω is the change of that image length in a unit of time. 
     When the physical size of the landmark is known, the distance to the landmark may also be determined based on the following equation: Z=f*W/ω, where f is the focal length, W is the size of the landmark (e.g., height or width), ω is the number of pixels when the landmark leaves the image. From the above equation, a change in distance Z may be calculated using ΔZ=f*W*Δω/ω 2 +f*ΔWω, where ΔW decays to zero by averaging, and where Δω is the number of pixels representing a bounding box accuracy in the image. A value estimating the physical size of the landmark may be calculated by averaging multiple observations at the server side. The resulting error in distance estimation may be very small. There are two sources of error that may occur when using the formula above, namely ΔW and Δω. Their contribution to the distance error is given by ΔZ=f*W*Δω/ω 2 +f*ΔW/ω. However, ΔW decays to zero by averaging; hence ΔZ is determined by Δω (e.g., the inaccuracy of the bounding box in the image). 
     For landmarks of unknown dimensions, the distance to the landmark may be estimated by tracking feature points on the landmark between successive frames. For example, certain features appearing on a speed limit sign may be tracked between two or more image frames. Based on these tracked features, a distance distribution per feature point may be generated. The distance estimate may be extracted from the distance distribution. For example, the most frequent distance appearing in the distance distribution may be used as the distance estimate. As another example, the average of the distance distribution may be used as the distance estimate. 
       FIG. 13  illustrates an example autonomous vehicle road navigation model represented by a plurality of three dimensional splines  1301 ,  1302 , and  1303 . The curves  1301 - 1303  shown in  FIG. 13  are for illustration purpose only. Each spline may include one or more three dimensional polynomials connecting a plurality of data points  1310 . Each polynomial may be a first order polynomial, a second order polynomial, a third order polynomial, or a combination of any suitable polynomials having different orders. Each data point  1310  may be associated with the navigation information received from vehicles  1205 - 1225 . In some embodiments, each data point  1310  may be associated with data related to landmarks (e.g., size, location, and identification information of landmarks) and/or road signature profiles (e.g., road geometry, road roughness profile, road curvature profile, road width profile). In some embodiments, some data points  1310  may be associated with data related to landmarks, and others may be associated with data related to road signature profiles. 
       FIG. 14  illustrates a block diagram of server  1230 . Server  1230  may include a communication unit  1405 , which may include both hardware components (e.g., communication control circuits, switches, and antenna), and software components (e.g., communication protocols, computer codes). Server  1230  may communicate with vehicles  1205 - 1225  through communication unit  1405 . For example, server  1230  may receive, through communication unit  1405 , navigation information transmitted from vehicles  1205 - 1225 . Server  1230  may distribute, through communication unit  1405 , the autonomous vehicle road navigation model to one or more autonomous vehicles. 
     Server  1230  may include one or more storage devices  1410 , such as a hard drive, a compact disc, a tape, etc. Storage device  1410  may be configured to store data, such as navigation information received from vehicles  1205 - 1225  and/or the autonomous vehicle road navigation model that server  1230  generates based on the navigation information. Storage device  1410  may be configured to store any other information, such as a sparse map (e.g., sparse map  800  discussed in connection with  FIG. 8 ). 
     In addition to or in place of storage device  1410 , server  1230  may include a memory  1415 . Memory  1415  may be similar to or different from memory  140  or  150 . Memory  1415  may be a non-transitory memory, such as a flash memory, a random access memory, etc. Memory  1415  may be configured to store data, such as computer codes or instructions executable by a processor (e.g., processor  1420 ), map data (e.g., data of sparse map  800 ), the autonomous vehicle road navigation model, and/or navigation information received from vehicles  1205 - 1225 . 
     Server  1230  may include a processor  1420  configured to execute computer codes or instructions stored in memory  1415  to perform various functions. For example, processor  1420  may analyze the navigation information received from vehicles  1205 - 1225 , and generate the autonomous vehicle road navigation model based on the analysis. Processor  1420  may control communication unit  1405  to distribute the autonomous vehicle road navigation model to one or more autonomous vehicles (e.g., one or more of vehicles  1205 - 1225  or any vehicle that travels on road segment  1200  at a later time). Processor  1420  may be similar to or different from processor  180 ,  190 , or processing unit  110 . 
       FIG. 15  illustrates a block diagram of memory  1415 , which may store computer codes or instructions for performing one or more operations for processing vehicle navigation information for use in autonomous vehicle navigation. As shown in  FIG. 15 , memory  1415  may store one or more modules for performing the operations for processing vehicle navigation information. For example, memory  1415  may include a model generating module  1505  and a model distributing module  1510 . Processor  1420  may execute the instructions stored in any of modules  1505  and  1510  included in memory  1415 . 
     Model generating module  1505  may store instructions which, when executed by processor  1420 , may generate at least a portion of an autonomous vehicle road navigation model for a common road segment (e.g., road segment  1200 ) based on navigation information received from vehicles  1205 - 1225 . For example, in generating the autonomous vehicle road navigation model, processor  1420  may cluster vehicle trajectories along the common road segment  1200  into different clusters. Processor  1420  may determine a target trajectory along the common road segment  1200  based on the clustered vehicle trajectories for each of the different clusters. Such an operation may include finding a mean or average trajectory of the clustered vehicle trajectories (e.g., by averaging data representing the clustered vehicle trajectories) in each cluster. In some embodiments, the target trajectory may be associated with a single lane of the common road segment  1200 . The autonomous vehicle road navigation model may include a plurality of target trajectories each associated with a separate lane of the common road segment  1200 . In some embodiments, the target trajectory may be associated with the common road segment  1200  instead of a single lane of the road segment  1200 . The target trajectory may be represented by a three dimensional spline. In some embodiments, the spline may be defined by less than 10 kilobytes per kilometer, less than 20 kilobytes per kilometer, less than 100 kilobytes per kilometer, less than 1 megabyte per kilometer, or any other suitable storage size per kilometer. 
     The road model and/or sparse map may store trajectories associated with a road segment. These trajectories may be referred to as target trajectories, which are provided to autonomous vehicles for autonomous navigation. The target trajectories may be received from multiple vehicles, or may be generated based on actual trajectories or recommended trajectories (actual trajectories with some modifications) received from multiple vehicles. The target trajectories included in the road model or sparse map may be continuously updated (e.g., averaged) with new trajectories received from other vehicles. 
     Vehicles traveling on a road segment may collect data by various sensors. The data may include landmarks, road signature profile, vehicle motion (e.g., accelerometer data, speed data), vehicle position (e.g., GPS data), and may either reconstruct the actual trajectories themselves, or transmit the data to a server, which will reconstruct the actual trajectories for the vehicles. In some embodiments, the vehicles may transmit data relating to a trajectory (e.g., a curve in an arbitrary reference frame), landmarks data, and lane assignment along traveling path to server  1230 . Various vehicles traveling along the same road segment at multiple drives may have different trajectories. Server  1230  may identify routes or trajectories associated with each lane from the trajectories received from vehicles through a clustering process. 
       FIG. 16  illustrates a process of clustering vehicle trajectories associated with vehicles  1205 - 1225  for determining a target trajectory for the common road segment (e.g., road segment  1200 ). The target trajectory or a plurality of target trajectories determined from the clustering process may be included in the autonomous vehicle road navigation model or sparse map  800 . In some embodiments, vehicles  1205 - 1225  traveling along road segment  1200  may transmit a plurality of trajectories  1600  to server  1230 . In some embodiments, server  1230  may generate trajectories based on landmark, road geometry, and vehicle motion information received from vehicles  1205 - 1225 . To generate the autonomous vehicle road navigation model, server  1230  may cluster vehicle trajectories  1600  into a plurality of clusters  1605 - 1630 , as shown in  FIG. 16 . 
     Clustering may be performed using various criteria. In some embodiments, all drives in a cluster may be similar with respect to the absolute heading along the road segment  1200 . The absolute heading may be obtained from GPS signals received by vehicles  1205 - 1225 . In some embodiments, the absolute heading may be obtained using dead reckoning Dead reckoning, as one of skill in the art would understand, may be used to determine the current position and hence heading of vehicles  1205 - 1225  by using previously determined position, estimated speed, etc. Trajectories clustered by absolute heading may be useful for identifying routes along the roadways. 
     In some embodiments, all the drives in a cluster may be similar with respect to the lane assignment (e.g., in the same lane before and after a junction) along the drive on road segment  1200 . Trajectories clustered by lane assignment may be useful for identifying lanes along the roadways. In some embodiments, both criteria (e.g., absolute heading and lane assignment) may be used for clustering. 
     In each cluster  1605 - 1630 , trajectories may be averaged to obtain a target trajectory associated with the specific cluster. For example, the trajectories from multiple drives associated with the same lane cluster may be averaged. The averaged trajectory may be a target trajectory associate with a specific lane. To average a cluster of trajectories, server  1230  may select a reference frame of an arbitrary trajectory C 0 . For all other trajectories (C 1 , . . . , Cn), server  1230  may find a rigid transformation that maps Ci to C 0 , where i=1, 2, . . . , n, where n is a positive integer number, corresponding to the total number of trajectories included in the cluster. Server  1230  may compute a mean curve or trajectory in the C 0  reference frame. 
     In some embodiments, the landmarks may define an arc length matching between different drives, which may be used for alignment of trajectories with lanes. In some embodiments, lane marks before and after a junction may be used for alignment of trajectories with lanes. 
     To assemble lanes from the trajectories, server  1230  may select a reference frame of an arbitrary lane. Server  1230  may map partially overlapping lanes to the selected reference frame. Server  1230  may continue mapping until all lanes are in the same reference frame. Lanes that are next to each other may be aligned as if they were the same lane, and later they may be shifted laterally. 
     Landmarks recognized along the road segment may be mapped to the common reference frame, first at the lane level, then at the junction level. For example, the same landmarks may be recognized multiple times by multiple vehicles in multiple drives. The data regarding the same landmarks received in different drives may be slightly different. Such data may be averaged and mapped to the same reference frame, such as the C 0  reference frame. Additionally or alternatively, the variance of the data of the same landmark received in multiple drives may be calculated. 
     In some embodiments, each lane of road segment  120  may be associated with a target trajectory and certain landmarks. The target trajectory or a plurality of such target trajectories may be included in the autonomous vehicle road navigation model, which may be used later by other autonomous vehicles traveling along the same road segment  1200 . Landmarks identified by vehicles  1205 - 1225  while the vehicles travel along road segment  1200  may be recorded in association with the target trajectory. The data of the target trajectories and landmarks may be continuously or periodically updated with new data received from other vehicles in subsequent drives. 
     For localization of an autonomous vehicle, the disclosed systems and methods may use an extended Kalman filter. The location of the vehicle may be determined based on three dimensional position data and/or three dimensional orientation data, prediction of future location ahead of vehicle&#39;s current location by integration of ego motion. The localization of vehicle may be corrected or adjusted by image observations of landmarks. For example, when vehicle detects a landmark within an image captured by the camera, the landmark may be compared to a known landmark stored within the road model or sparse map  800 . The known landmark may have a known location (e.g., GPS data) along a target trajectory stored in the road model and/or sparse map  800 . Based on the current speed and images of the landmark, the distance from the vehicle to the landmark may be estimated. The location of the vehicle along a target trajectory may be adjusted based on the distance to the landmark and the landmark&#39;s known location (stored in the road model or sparse map  800 ). The landmark&#39;s position/location data (e.g., mean values from multiple drives) stored in the road model and/or sparse map  800  may be presumed to be accurate. 
     In some embodiments, the disclosed system may form a closed loop subsystem, in which estimation of the vehicle six degrees of freedom location (e.g., three dimensional position data plus three dimensional orientation data) may be used for navigating (e.g., steering the wheel of) the autonomous vehicle to reach a desired point (e.g., 1.3 second ahead in the stored). In turn, data measured from the steering and actual navigation may be used to estimate the six degrees of freedom location. 
     In some embodiments, poles along a road, such as lampposts and power or cable line poles may be used as landmarks for localizing the vehicles. Other landmarks such as traffic signs, traffic lights, arrows on the road, stop lines, as well as static features or signatures of an object along the road segment may also be used as landmarks for localizing the vehicle. When poles are used for localization, the x observation of the poles (i.e., the viewing angle from the vehicle) may be used, rather than the y observation (i.e., the distance to the pole) since the bottoms of the poles may be occluded and sometimes they are not on the road plane. 
       FIG. 17  illustrates a navigation system for a vehicle, which may be used for autonomous navigation. For illustration, the vehicle is referenced as vehicle  1205 . The vehicle shown in  FIG. 17  may be any other vehicle disclosed herein, including, for example, vehicles  1210 ,  1215 ,  1220 , and  1225 , as well as vehicle  200  shown in other embodiments. As shown in  FIG. 12 , vehicle  1205  may communicate with server  1230 . Vehicle  1205  may include an image capture device  122  (e.g., camera  122 ). Vehicle  1205  may include a navigation system  1700  configured for providing navigation guidance for vehicle  1205  to travel on a road (e.g., road segment  1200 ). Vehicle  1205  may also include other sensors, such as a speed sensor  1720  and an accelerometer  1725 . Speed sensor  1720  may be configured to detect the speed of vehicle  1205 . Accelerometer  1725  may be configured to detect an acceleration or deceleration of vehicle  1205 . Vehicle  1205  shown in  FIG. 17  may be an autonomous vehicle, and the navigation system  1700  may be used for providing navigation guidance for autonomous driving. Alternatively, vehicle  1205  may also be a non-autonomous, human-controlled vehicle, and navigation system  1700  may still be used for providing navigation guidance. 
     Navigation system  1700  may include a communication unit  1705  configured to communicate with server  1230  through communication path  1235 . Navigation system  1700  may include a GPS unit  1710  configured to receive and process GPS signals. Navigation system  1700  may include at least one processor  1715  configured to process data, such as GPS signals, map data from sparse map  800  (which may be stored on a storage device provided onboard vehicle  1205  or received from server  1230 ), road geometry sensed by a road profile sensor  1730 , images captured by camera  122 , and/or autonomous vehicle road navigation model received from server  1230 . The road profile sensor  1730  may include different types of devices for measuring different types of road profile, such as road surface roughness, road width, road elevation, road curvature, etc. For example, the road profile sensor  1730  may include a device that measures the motion of a suspension of vehicle  1205  to derive the road roughness profile. In some embodiments, the road profile sensor  1730  may include radar sensors to measure the distance from vehicle  1205  to road sides (e.g., barrier on the road sides), thereby measuring the width of the road. In some embodiments, the road profile sensor  1730  may include a device configured for measuring the up and down elevation of the road. In some embodiment, the road profile sensor  1730  may include a device configured to measure the road curvature. For example, a camera (e.g., camera  122  or another camera) may be used to capture images of the road showing road curvatures. Vehicle  1205  may use such images to detect road curvatures. 
     The at least one processor  1715  may be programmed to receive, from camera  122 , at least one environmental image associated with vehicle  1205 . The at least one processor  1715  may analyze the at least one environmental image to determine navigation information related to the vehicle  1205 . The navigation information may include a trajectory related to the travel of vehicle  1205  along road segment  1200 . The at least one processor  1715  may determine the trajectory based on motions of camera  122  (and hence the vehicle), such as three dimensional translation and three dimensional rotational motions. In some embodiments, the at least one processor  1715  may determine the translation and rotational motions of camera  122  based on analysis of a plurality of images acquired by camera  122 . In some embodiments, the navigation information may include lane assignment information (e.g., in which lane vehicle  1205  is traveling along road segment  1200 ). The navigation information transmitted from vehicle  1205  to server  1230  may be used by server  1230  to generate and/or update an autonomous vehicle road navigation model, which may be transmitted back from server  1230  to vehicle  1205  for providing autonomous navigation guidance for vehicle  1205 . 
     The at least one processor  1715  may also be programmed to transmit the navigation information from vehicle  1205  to server  1230 . In some embodiments, the navigation information may be transmitted to server  1230  along with road information. The road location information may include at least one of the GPS signal received by the GPS unit  1710 , landmark information, road geometry, lane information, etc. The at least one processor  1715  may receive, from server  1230 , the autonomous vehicle road navigation model or a portion of the model. The autonomous vehicle road navigation model received from server  1230  may include at least one update based on the navigation information transmitted from vehicle  1205  to server  1230 . The portion of the model transmitted from server  1230  to vehicle  1205  may include an updated portion of the model. The at least one processor  1715  may cause at least one navigational maneuver (e.g., steering such as making a turn, braking, accelerating, passing another vehicle, etc.) by vehicle  1205  based on the received autonomous vehicle road navigation model or the updated portion of the model. 
     The at least one processor  1715  may be configured to communicate with various sensors and components included in vehicle  1205 , including communication unit  1705 , GPS unit  1715 , camera  122 , speed sensor  1720 , accelerometer  1725 , and road profile sensor  1730 . The at least one processor  1715  may collect information or data from various sensors and components, and transmit the information or data to server  1230  through communication unit  1705 . Alternatively or additionally, various sensors or components of vehicle  1205  may also communicate with server  1230  and transmit data or information collected by the sensors or components to server  1230 . 
     In some embodiments, vehicles  1205 - 1225  may communicate with each other, and may share navigation information with each other, such that at least one of the vehicles  1205 - 1225  may generate the autonomous vehicle road navigation model based on information shared by other vehicles. In some embodiments, vehicles  1205 - 1225  may share navigation information with each other and each vehicle may update its own the autonomous vehicle road navigation model provided in the vehicle. In some embodiments, at least one of the vehicles  1205 - 1225  (e.g., vehicle  1205 ) may function as a hub vehicle. The at least one processor  1715  of the hub vehicle (e.g., vehicle  1205 ) may perform some or all of the functions performed by server  1230 . For example, the at least one processor  1715  of the hub vehicle may communicate with other vehicles and receive navigation information from other vehicles. The at least one processor  1715  of the hub vehicle may generate the autonomous vehicle road navigation model or an update to the model based on the shared information received from other vehicles. The at least one processor  1715  of the hub vehicle may transmit the autonomous vehicle road navigation model or the update to the model to other vehicles for providing autonomous navigation guidance. 
       FIG. 18  is a flowchart showing an example process  1800  for processing vehicle navigation information for use in autonomous vehicle navigation. Process  1800  may be performed by server  1230  or processor  1715  included in a hub vehicle. In some embodiments, process  1800  may be used for aggregating vehicle navigation information to provide an autonomous vehicle road navigation model or to update the model. Process  1800  may include receiving navigation information from a plurality of vehicles (step  1805 ). For example, server  1230  may receive the navigation information from vehicles  1205 - 1225 . The navigation information may be associated with a common road segment (e.g., road segment  1200 ) along which the vehicles  1205 - 1225  travel. Process  1800  may include storing the navigation information associated with the common road segment (step  1810 ). For example, server  1230  may store the navigation information in storage device  1410  and/or memory  1415 . Process  1800  may include generating at least a portion of an autonomous vehicle road navigation model based on the navigation information (step  1815 ). For example, server  1230  may generate at least a portion of the autonomous vehicle road navigation model for common road segment  1200  based on the navigation information received from vehicles  1205 - 1225  that travel on the common road segment  1200 . Process  1800  may further include distributing the autonomous vehicle road navigation model to one or more autonomous vehicles (step  1820 ). For example, server  1230  may distribute the autonomous vehicle road navigation model or a portion (e.g., an update) of the model to vehicles  1205 - 1225 , or any other vehicles later travel on road segment  1200  for use in autonomously navigating the vehicles along road segment  1200 . 
     Process  1800  may include additional operations or steps. For example, generating the autonomous vehicle road navigation model may include clustering vehicle trajectories received from vehicles  1205 - 1225  along road segment  1200  into a plurality of clusters. Process  1800  may include determining a target trajectory along common road segment  1200  by averaging the clustered vehicle trajectories in each cluster. Process  1800  may also include associating the target trajectory with a single lane of common road segment  1200 . Process  1800  may include determining a three dimensional spline to represent the target trajectory in the autonomous vehicle road navigation model. 
       FIG. 19  is a flowchart showing an example process  1900  performed by a navigation system of a vehicle. Process  1900  may be performed by processor  1715  included in navigation system  1700 . Process  1900  may include receiving, from a camera, at least one environmental image associated with the vehicle (step  1905 ). For example, processor  1715  may receive, from camera  122 , at least one environmental image associated with vehicle  1205 . Camera  122  may capture one or more images of the environment surrounding vehicle  1205  as vehicle  1205  travels along road segment  1200 . Process  1900  may include analyzing the at least one environmental image to determine navigation information related to the vehicle (step  1910 ). For example, processor  1715  may analyze the environmental images received from camera  122  to determine navigation information, such as a trajectory of travel along road segment  1200 . Processor  1715  may determine the trajectory of travel of vehicle  1205  based on camera ego motions (e.g., three dimensional translation and/or three dimensional rotational motions) sensed by, e.g., the analysis of the images. 
     Process  1900  may include transmitting the navigation information from the vehicle to a server (step  1915 ). In some embodiments, the navigation information may be transmitted along with road information from the vehicle to server  1230 . For example, processor  1715  may transmit, via communication unit  1705 , the navigation information along with road information, such as the lane assignment, road geometry, from vehicle  1205  to server  1230 . Process  1900  may include receiving from the server an autonomous vehicle road navigation model or a portion of the model (step  1920 ). For example, processor  1715  may receive the autonomous vehicle road navigation model or a portion of the model from server  1230 . The model or the portion of the model may include at least one update to the model based on the navigation information transmitted from vehicle  1205 . Processor  1715  may update an existing model provided in navigation system  1700  of vehicle  1205 . Process  1900  may include causing at least one navigational maneuver by the vehicle based on the autonomous vehicle road navigation model (step  1925 ). For example, processor  1715  may cause vehicle  1205  to steer, make a turn, change lanes, accelerate, brake, stop, etc. Processor  1715  may send signals to at least one of throttling system  220 , braking system  230 , and steering system  240  to cause vehicle  1205  to perform the navigational maneuver. 
     Process  1900  may include other operations or steps performed by processor  1715 . For example, the navigation information may include a target trajectory for vehicles to travel along a road segment, and process  1900  may include clustering, by processor  1715 , vehicle trajectories related to multiple vehicles traveling on the road segment and determining the target trajectory based on the clustered vehicle trajectories. Clustering vehicle trajectories may include clustering, by processor  1715 , the multiple trajectories related to the vehicles traveling on the road segment into a plurality of clusters based on at least one of the absolute heading of vehicles or lane assignment of the vehicles. Generating the target trajectory may include averaging, by processor  1715 , the clustered trajectories. Other processes or steps performed by server  1230 , as described above, may also be included in process  1900 . 
     The disclosed systems and methods may include other features. For example, the disclosed systems may use local coordinates, rather than global coordinates. For autonomous driving, some systems may present data in world coordinates. For example, longitude and latitude coordinates on the earth surface may be used. In order to use the map for steering, the host vehicle must know its position and orientation relative to the map. It seems natural to use a GPS device on board, in order to position the vehicle on the map and in order to find the rotation transformation between the body reference frame and the world reference frame (say, North, East and Down). Once the body reference frame is aligned with the map reference frame, then the desired route may be expressed in the body reference frame and the steering commands may be computed or generated. 
     However, one possible issue with this strategy is that current GPS technology does not usually provide the body location and pose with sufficient accuracy and availability. To overcome this problem, it has been proposed to use landmarks whose world coordinates are known. The idea is to construct very detailed maps (called High Definition or HD maps), that contain landmarks of different kinds. The assumption is that the vehicle is equipped with a sensor that can detect and locate the landmarks in its own reference frame. Once the relative position between the vehicle and the landmarks is found, the landmarks&#39; world coordinates are taken from the HD map, and the vehicle can use them to compute its own location and pose. 
     This method is still using the global world coordinate system as a mediator that establishes the alignment between the map and the body reference frames. Namely, the landmarks are used in order to compensate for the limitations of the GPS device onboard the vehicles. The landmarks, together with an HD map, may enable to compute the precise vehicle pose in global coordinates, and hence the map-body alignment problem is solved. 
     In the disclosed systems and methods, instead of using one global map of the world, many map pieces or local maps may be used for autonomous navigation. Each piece of a map or each local map may define its own coordinate frame. These coordinate frames may be arbitrary. The vehicle&#39;s coordinates in the local maps may not need to indicate where the vehicle is located on the surface of earth. Moreover, the local maps may not be required to be accurate over large scales, meaning there may be no rigid transformation that can embed a local map in the global world coordinate system. 
     There are two main processes associated with this representation of the world, one relates to the generation of the maps and the other relates to using them. With respect to maps generation, this type of representation may be created and maintained by crowd sourcing. There may be no need to apply sophisticated survey equipment, because the use of HD maps is limited, and hence crowd sourcing becomes feasible. With respect to usage, an efficient method to align the local map with the body reference frame without going through a standard world coordinate system may be employed. Hence there may be no need, at least in most scenarios and circumstances, to have a precise estimation of the vehicle location and pose in global coordinates. The memory footprint of the local maps may be kept very small. 
     The principle underlying the maps generation is the integration of ego motion. The vehicles sense the motion of the camera in space (3D translation and 3D rotation). The vehicles or the server may reconstruct the trajectory of the vehicle by integration of ego motion over time, and this integrated path may be used as a model for the road geometry. This process may be combined with sensing of close range lane marks, and then the reconstructed route may reflect the path that a vehicle should follow, and not the particular path that it did follow. In other words, the reconstructed route or trajectory may be modified based on the sensed data relating to close range lane marks, and the modified reconstructed trajectory may be used as a recommended trajectory or target trajectory, which may be saved in the road model or sparse map for use by other vehicles navigating the same road segment. 
     In some embodiments, the map coordinate system may be arbitrary. A camera reference frame may be selected at an arbitrary time, and used as the map origin. The integrated trajectory of the camera may be expressed in the coordinate system of that particular chosen frame. The value of the route coordinates in the map may not directly represent a location on earth. 
     The integrated path may accumulate errors. This may be due to the fact that the sensing of the ego motion may not be absolutely accurate. The result of the accumulated error is that the local map may diverge, and the local map may not be regarded as a local copy of the global map. The larger the size of the local map piece, the larger the deviation from the “true” geometry on earth. 
     The arbitrariness and the divergence of the local maps may not be a design principle but rather may be a consequence. These properties may be a consequence of the integration method, which may be applied in order to construct the maps in a crowd sourcing manner (by vehicles traveling along the roads). However, vehicles may successfully use the local maps for steering. 
     The proposed map may diverge over long distances. Since the map is used to plan a trajectory in the immediate vicinity of the vehicle, the effect of the divergence may be acceptable. At any time instance, the system (e.g., server  1230  or vehicle  1205 ) may repeat the alignment procedure, and use the map to predict the road location (in the camera coordinate frame) some 1.3 seconds ahead (or any other seconds, such as 1.5 seconds, 1.0 second, 1.8 seconds, etc.). As long as the accumulated error over that distance is small enough, then the steering command provided for autonomous driving may be used. 
     In some embodiments, a local map may focus on a local area, and may not cover a too large area. This means that a vehicle that is using a local map for steering in autonomous driving, may arrive at some point to the end of the map and may have to switch to another local piece of map. The switching may be enabled by the local maps overlapping each other. Once the vehicle enters the area that is common to both maps, the system (e.g., server  1230  or vehicle  1205 ) may continue to generate steering commands based on a first local map (the map that is being used), but at the same time the system may localize the vehicle on the other map (or second local map) that overlaps with the first local map. In other words, the system may simultaneously align the present coordinate frame of the camera both with the coordinate frame of the first map and with the coordinate frame of the second map. When the new alignment is established, the system may switch to the other map and plan the vehicle trajectory there. 
     The disclosed systems may include additional features, one of which is related to the way the system aligns the coordinate frames of the vehicle and the map. As explained above that landmarks may be used for alignment, assuming the vehicle may measure its relative position to them. This is useful in autonomous driving, but sometimes it may result in a demand for a large number of landmarks and hence a large memory footprint. The disclosed systems may therefore use an alignment procedure that addresses this problem. In the alignment procedure, the system may compute a 1D estimator for the location of the vehicle along the road, using sparse landmarks and integration of ego speed. The system may use the shape of the trajectory itself to compute the rotation part of the alignment, using a tail alignment method discussed in details below in other sections. The idea is that the vehicle reconstructs its own trajectory while driving the “tail” and computes a rotation around its assumed position along the road, in order to align the tail with the map. 
     In the disclosed systems and methods, a GPS device may still be used. Global coordinates may be used for indexing the database that stores the trajectories and/or landmarks. The relevant piece of local map and the relevant landmarks in the vicinity of the vehicles may be stored in memory and retrieved from the memory using global GPS coordinates. However, in some embodiments, the global coordinates may not be used for path planning, and may not be accurate. In one example, the usage of global coordinates may be limited for indexing of the information. 
     In situations where “tail alignment” cannot function well, the system may compute the vehicle&#39;s pose using a larger number of landmarks. This may be a rare case, and hence the impact on the memory footprint may be moderate. Road intersections are examples of such situations. 
     The disclosed systems and methods may use semantic landmarks (e.g., traffic signs), since they can be reliably detected from the scene and matched with the landmarks stored in the road model or sparse map. In some cases the disclosed systems may use non-semantic landmarks (e.g., general purpose signs) as well, and in such cases the non-semantic landmarks may be attached to an appearance signature, as discussed above. The system may use a learning method for the generation of signatures that follows the “same or not-same” recognition paradigm. 
     For example, given many drives with GPS coordinates along them, the disclosed systems may produce the underlying road structure junctions and road segments. The roads are assumed to be far enough from each other to be able to differentiate them using the GPS. Only a coarse grained map may be needed. To generate the underlying road structure graph, the space may be divided into a lattice of a given resolution (e.g., 50 m by 50 m). Every drive may be seen as an ordered list of lattice sites. The system may color every lattice site belonging to a drive to produce an image of the merged drives. The colored lattice points may be represented as nodes on the merged drives. The drives passing from one node to another may be represented as links. The system may fill small holes in the image, to avoid differentiating lanes and correct for GPS errors. The system may use a suitable thinning algorithm (e.g., an algorithm named “Zhang-Suen” thinning algorithm) to obtain the skeleton of the image. This skeleton may represent the underlying road structure, and junctions may be found using a mask (e.g., a point connected to at least three others). After the junctions are found, the segments may be the skeleton parts that connect them. To match the drives back to the skeleton, the system may use a Hidden Markov Model. Every GPS point may be associated with a lattice site with a probability inverse to its distance from that site. Use a suitable algorithm (e.g., an algorithm named the “Viterbi” algorithm) to match GPS points to lattice sites, while not allowing consecutive GPS points to match to non neighboring lattice sites. 
     A plurality of methods may be used for mapping the drives back to the map. For example, a first solution may include keeping track during the thinning process. A second solution may use proximity matching. A third solution may use hidden Markov model. The hidden Markov model assumes an underlying hidden state for every observation, and assigns probabilities for a given observation given the state, and for a state given the previous state. A Viterbi algorithm may be used to find the most probable states given a list of observations. 
     The disclosed systems and methods may include additional features. For example, the disclosed systems and methods may detect highway entrances/exits. Multiple drives in the same area may be merged using GPS data to the same coordinate system. The system may use visual feature points for mapping and localization. 
     In some embodiments, generic visual features may be used as landmarks for the purpose of registering the position and orientation of a moving vehicle, in one drive (localization phase), relative to a map generated by vehicles traversing the same stretch of road in previous drives (mapping phase). These vehicles may be equipped with calibrated cameras imaging the vehicle surroundings and GPS receivers. The vehicles may communicate with a central server (e.g., server  1230 ) that maintains an up-to-date map including these visual landmarks connected to other significant geometric and semantic information (e.g. lane structure, type and position of road signs, type and position of road marks, shape of nearby drivable ground area delineated by the position of physical obstacles, shape of previously driven vehicle path when controlled by human driver, etc.). The total amount of data that may be communicated between the central server and vehicles per length of road is small, both in the mapping and localization phases. 
     In the mapping phase, the disclosed systems (e.g., vehicles or server) may detect feature points (FPs) and compute their descriptors (e.g. using the FAST/BRISK/ORB detectors and descriptors or a detector/descriptor pair that was trained using the database discussed below). The system may track FPs between frames in which they appear using their motion in the image plane and by matching their descriptors using e.g. Euclidean or Hamming distance in descriptor space. The system may use tracked FPs to estimate camera motion and world positions of objects on which FPs were detected and tracked. The system may classify FPs as ones that will likely be detected in future drives (e.g. FPs detected on momentarily moving objects, parked cars and shadow texture will likely not reappear in future drives). This reproducibility classification (RC) may be a function of both the intensities in a region of the image pyramid surrounding the detected FP, the motion of the tracked FP in the image plane, the extent of viewpoints in which it was successfully tracked/detected and its relative 3D position. In some embodiments, the vehicles may send representative FP descriptors (computed from a set of observations), estimated 3D position relative to vehicle and momentary vehicle GPS coordinates to server  1230 . 
     During the mapping phase, when communication bandwidth between the mapping vehicles and central server is limited, the vehicles may send FPs to the server at a high frequency when the presence of FPs or other semantic landmarks in the map (such as road signs and lane structure) is limited and insufficient for the purpose of localization. Although vehicles in the mapping phase may send FPs at a low spatial frequency these may be agglomerated in the server. Detection of reoccurring FPs may also be performed by the server and the server may store the set of reoccurring FPs. Visual appearance of landmarks may at least in some cases be sensitive to the time of day or the season in which they were captured. To increase reproducibility probability of FPs, these may be binned by the server into time-of-day and season bins. 
     The vehicles may send the server other semantic and geometric information in the nearby FP coordinate system (lane shape, structure of road plane, 3D position of obstacles, free space in mapping clip momentary coordinate system, path driven by human driver in a setup drive to a parking location). 
     In a localization phase, the server may send a map containing landmarks in the form of FP positions and descriptors to vehicles. Feature points (FPs) may be detected and tracked in near real time within a set of current consecutive frames. Tracked FPs may be used to estimate camera motion and world positions of FPs. Currently detected FP descriptors may be searched to match a list of map FPs having GPS coordinates within an estimated finite GPS uncertainty radius from the momentary GPS reading. Matching may be done by searching all pairs of current and mapping FPs that minimize an Euclidean or Hamming distance in descriptor space. Using the FP matches and their current and map positions, rotation and translation between the momentary vehicle position and the local map coordinate system may be registered. 
     The disclosed systems and methods may include a method for training a reproducibility classifier. Training may be performed in one of the following schemes in order of growing labeling cost and resulting classifier accuracy. 
     In the first scheme, a database including a large number of clips recorded by vehicle cameras with matching momentary vehicle GPS position may be collected. This database may include a representative sample of drives (with respect to various properties: e.g., time of day, season, weather condition, type of roadway). Feature points (FPs) extracted from frames of different drives at a similar GPS position and heading may be potentially matched within a GPS uncertainty radius. Unmatched FPs may be labeled unreproducible and those matched may be labeled reproducible. A classifier may then be trained to predict the reproducibility label of an FP given its appearance in the image pyramid, its momentary position relative to the vehicle and the extent of viewpoints positions in which it was successfully tracked. 
     In the second scheme, FP pairs extracted from the clip database described in the first scheme may also be labeled by a human responsible for annotating FP matches between clips. 
     In a third scheme, a database augmenting that of the first scheme with precise vehicle position, vehicle orientation and image pixel depth using Light Detection And Ranging (LIDAR) measurements may be used to accurately match world positions in different drives. Feature point descriptors may then be computed at the image region corresponding to these world points at different viewpoints and drive times. The classifier may then be trained to predict the average distance in descriptor space a descriptor is located from its matched descriptors. In this case reproducibility may be measured by likely having a low descriptor distance. 
     Uploading Recommended, Not Actual Trajectories 
     Consistent with disclosed embodiments, the system may generate an autonomous vehicle road navigation model based on the observed trajectories of vehicles traversing a common road segment (e.g., which may correspond to the trajectory information forwarded to a server by a vehicle). The observed trajectories, however, may not correspond to actual trajectories taken by vehicles traversing a road segment. Rather, in certain situations, the trajectories uploaded to the server may be modified with respect to actual reconstructed trajectories determined by the vehicles. For example, a vehicle system, while reconstructing a trajectory actually taken, may use sensor information (e.g., analysis of images provided by a camera) to determine that its own trajectory may not be the preferred trajectory for a road segment. For example, the vehicle may determine based on image data from onboard cameras that it is not driving in a center of a lane or that it crossed over a lane boundary for a determined period of time. In such cases, among others, a refinement to the vehicle&#39;s reconstructed trajectory (the actual path traversed) may be made based on information derived from the sensor output. The refined trajectory, not the actual trajectory, may then be uploaded to the server for potential use in building or updating sparse data map  800 . 
     Referring to  FIGS. 12 and 17 , vehicle  1205  may communicate with server  1230 . Vehicle  1205  may be an autonomous vehicle or a traditional, primarily human-controlled vehicle. Vehicle  1205  may collect (or detect, sense, measure) data regarding road segment  1200  as vehicle  1205  travels along road segment  1200 . The collected data may include navigation information, such as road geometry, recognized landmark including signs, road markings, etc. Vehicle  1205  may transmit the collected data to server  1230 . Server  1230  may generate and/or update an autonomous vehicle road navigation model based on the data received from vehicle  1205 . The autonomous vehicle road navigation model may include a plurality of target trajectories representing preferred paths of travel along particular road segments. 
     As shown in  FIG. 17 , vehicle  1205  may include navigation system  1700 . Navigation system may include a storage device (e.g., a hard drive, a memory) configured for storing the autonomous vehicle road navigation model and/or map data (e.g., map data of sparse map  800 ). It should be noted that the storage device may store a local copy of the entire road model from sparse data map  800 . Alternately, the storage device may store only portions of sparse data maps (e.g., local maps) provided to the navigating vehicle as needed. In such embodiments, the local maps may be stored only temporarily in the storage device and may be purged from the storage device upon receipt of one or more newly received local maps or after a vehicle is determined to have exited a particular navigational area or zone. Navigation system  1700  may include at least one processor  1715 . 
     Navigation system  1700  may include one or more sensors, such as camera  122 , GPS unit  1710 , road profile sensor  1730 , speed sensor  1720 , and accelerometer  1725 . Vehicle  1205  may include other sensors, such as radar sensors. The sensors included in vehicle  1205  may collect data related to road segment  1200  as vehicle  1205  travels along road segment  1200 . 
     The processor  1715  may be configured to receive, from the one or more sensors, outputs indicative of a motion of vehicle  1205 . For example, accelerometer  1725  may output signals indicating three dimensional translation and/or three dimensional rotational motions of camera  122 . Speed sensor may output a speed of vehicle  1205 . Road profile sensor  1730  may output signals indicating road roughness, road width, road elevation, road curvature, which may be used to determine the motion or trajectory of the vehicle  1205 . 
     Processor  1715  may determine an actual trajectory of vehicle  1205  based on the outputs from the one or more sensors. For example, based on analysis of images output from camera  122 , processor  1715  may identify landmarks along road segment  1200 . Landmarks may include traffic signs (e.g., speed limit signs), directional signs (e.g., highway directional signs pointing to different routes or places), and general signs (e.g., a rectangular business sign that is associated with a unique signature, such as a color pattern). The identified landmark may be compared with the landmark stored in sparse map  800 . When a match is found, the location of the landmark stored in sparse map  800  may be used as the location of the identified landmark. The location of the identified landmark may be used for determining the location of the vehicle  1205  along a target trajectory. In some embodiments, processor  1715  may also determine the location of vehicle  1205  based on GPS signals output by GPS unit  1710 . 
     Processor  1715  may determine the vehicle motion based on output from the accelerometer  1725 , the camera  122 , and/or the speed sensor  1720 . For example, speed sensor  1720  may output a current speed of vehicle  1205  to processor  1715 . Accelerometer  1725  may output a signal indicating three dimensional translation and/or rotation of vehicle  1205  to processor  1715 . The camera  122  may output a plurality of images of the surrounding of vehicle  1205  to processor  1715 . Based on the outputs from the plurality of sensors and devices, processor  1715  may determine an actual trajectory of vehicle  1205 . The actual trajectory reflects the actual path vehicle  1205  has taken or is taking, including, e.g., which lane along road segment  1200  vehicle  1205  has traveled in or is traveling in, and what different road segments vehicle  1205  have traveled along. 
     Processor  1715  may receive, from camera  122 , at least one environmental image associated with vehicle  1205 . For example, camera  122  may be a front-facing camera, which may capture an image of the environment in front of vehicle  1205 . Camera  122  may be facing other directions, such as the sides of vehicle  1205  or the rear of vehicle  1205 . Vehicle  1205  may include a plurality of cameras facing different directions. Processor  1715  may analyze the at least one environmental image to determine information associated with at least one navigational constraint. The navigational constraint may include at least one of a barrier (e.g., a lane separating barrier), an object (e.g., a pedestrian, a lamppost, a traffic light post), a lane marking (e.g., a solid yellow lane marking), a sign (e.g., a traffic sign, a directional sign, a general sign), or another vehicle (e.g., a leading vehicle, a following vehicle, a vehicle that is traveling on the side of vehicle  1205 ). 
     Processor  1715  may also determine a target trajectory for transmitting to server  1230 . The target trajectory may be the same as the actual trajectory determined by processor  1715  based on the sensor outputs. In some embodiments, the target trajectory may be different from the actual trajectory determined based on the sensor outputs. The target trajectory may include one or more modifications to the actual trajectory based on the determined information associated with the at least one navigational constraint. 
     For example, the environmental image captured by camera  122  may include a barrier, such as a temporary lane shifting barrier  100  meters ahead of vehicle  1250  that changes the lanes (e.g., when lanes are temporarily shifted due to an accident ahead). Processor  1715  may detect the temporary lane shifting barrier from the image, and take a lane different from a lane corresponding to the target trajectory stored in the road model or sparse map in compliance to the temporary lane shift. The actual trajectory of vehicle may reflect this change of lanes. However, the lane shifting is temporary and may be cleared in the next 10, 15, or 30 minutes. Vehicle  1205  may thus modify the actual trajectory (i.e., the shift of lanes) vehicle  1205  has taken to reflect that a target trajectory should be different from the actual trajectory vehicle  1205  has taken. For example, the system may recognize that the path traveled differs from a preferred trajectory for the road segment. Thus, the system may adjust a reconstructed trajectory prior to uploading the trajectory information to the servers. In other embodiments, the actual reconstructed trajectory information may be uploaded, by one or more recommended trajectory refinements (e.g., a size and direction of a translation to be made to at least a portion of the reconstructed trajectory) may also be uploaded. In some embodiments, processor  1715  may transmit a modified actual trajectory to server  1230 . Server  1230  may generate or update a target trajectory based on the received information and may transmit the target trajectory to other autonomous vehicles that later travel on the same road segment. 
     As another example, the environmental image may include an object, such as a pedestrian suddenly appearing in road segment  1200 . Processor  1715  may detect the pedestrian, and vehicle  1205  may change lanes to avoid a collision with the pedestrian. The actual trajectory vehicle  1205  reconstructed based on sensed data may include the change of lanes. However, the pedestrian may soon leave the roadway. So, vehicle  1205  may modify the actual trajectory (or determine a recommended modification) to reflect that the target trajectory should be different from the actual trajectory taken (as the appearance of the pedestrian is a temporary condition that should not be accounted for in the target trajectory determination. In some embodiments, the vehicle may transmit to the server data indicating a temporary deviation from the predetermined trajectory, when the actual trajectory is modified. The data may indicate a cause of the deviation, or the server may analyze the data to determine a cause of the deviation. Knowing the cause of the deviation may be useful. For example, when the deviation is due to the driver noticing an accident that has recently occurred and, in response steering the wheel to avoid collision, the server may plan a more moderate adjustment to the model or a specific trajectory associated with the road segment based on the cause of deviation. As another example, when the cause of deviation is a pedestrian crossing the road, the server may determine that there is no need to change the trajectory in the future. 
     As another example, the environmental image may include a lane marking indicating that vehicle  1205  is driving slightly outside of a lane, perhaps under the control of a human driver. Processor  1715  may detect the lane marking from the captured images and may modify the actual trajectory of vehicle  1205  to account for the departure from the lane. For example, a translation may be applied to the reconstructed trajectory so that it falls within the center of an observed lane. 
       FIG. 20  shows an example memory  2000 . Memory  2000  may include various modules, which when executed by a processor, may cause the processor to perform the disclosed methods. For example, memory  2000  may include an actual trajectory determination module  2005 . Actual trajectory determination module  2005 , when executed by a processor (e.g., processor  1715  or other processors), may cause the processor to determine an actual trajectory of a vehicle based on data output or received from one or more sensors included in the vehicle. For example, the processor may reconstruct the actual trajectory based on signals received from one or more of accelerometer  1725 , camera  122 , and/or speed sensor  1720 . In some embodiments, the processor may determine the actual trajectory based on the outputs received from the sensors indicative of a motion of the vehicle. 
     Memory  2000  may also include a target trajectory determination module  2010 . Target trajectory determination module  2010 , when executed by the processor, may cause the processor to determine a target trajectory based on the actual trajectory. For example, based on data received from the sensor, the processor may determine that one or more modifications need to be made to the actual trajectory. The modified actual trajectory may be used as the target trajectory for transmitting to a server (e.g., server  1230 ). The target trajectory may represent a better trajectory than the actual trajectory for other autonomous vehicles to follow when the other autonomous vehicles travel on the same road segment at a later time. In some embodiments, the processor may determine a target trajectory that includes the actual trajectory and one or more modifications based on information associated with navigational constraints. 
     Memory  2000  may also include an image analysis module  2015 . Image analysis module, when executed by the processor, may cause the processor to analyze one or more images captured by a camera (e.g., camera  122 ) using various image analysis algorithms. For example, the processor may analyze an image of the environment to identify a landmark, at least one navigational constraint, or to calculate a distance from the vehicle to the landmark, etc. 
       FIG. 21  is a flowchart illustrating an example process for uploading recommended trajectory to a server. Process  2100  may be performed by a processor included in a navigation system of a vehicle, such as processor  1715  included in navigation system  1700  of autonomous vehicle  1205 . Process  2100  may include receiving, from one or more sensors, outputs indicative of a motion of a vehicle (step  2105 ). For example, processor  1715  may receive outputs from inertial sensors, such as accelerometer  1725  indicating the three dimensional translation and/or three dimensional rotational motions of vehicle  1205 . Process  2100  may include determining an actual trajectory of the vehicle based on the outputs from the one or more sensors (step  2110 ). For example, processor  1715  may analyze images from camera  122 , speed from speed sensor  1720 , position information from GPS unit  1710 , motion data from accelerometer  1725 , to determine an actual trajectory. Process  2100  may include receiving, from the camera, at least one environmental image associated with the vehicle (step  2115 ). For example, processor  1715  may receive at least one environmental image associated with vehicle  1205  from camera  122 . Camera  122  may be a front-facing camera, which may capture an image of an environment in front of vehicle  1205 . Process  2100  may include analyzing the at least one environmental image to determine information associated with at least one navigation constraint (step  2120 ). For example, processor  1715  may analyze the environmental images from camera  122  to detect at least one of a barrier, an object, a lane marking, a sign, or another vehicle in the images. Process  2100  may also include determining a target trajectory, including the actual trajectory and one or more modifications to the actual trajectory based on the determined information associated with the navigational constraint (step  2125 ). For example, based on at least one of the barrier, object, lane marking, sign, or another vehicle detected from the environmental images, processor  1715  may modify the actual trajectory, e.g., to include a lane or a road other than the lane or road vehicle  1205  is traveling in. The modified actual trajectory may be used as the target trajectory. The target trajectory may reflect a safer or better trajectory than the actual trajectory vehicle  1205  is taking. Process  2100  may further include transmitting the target trajectory to a server (step  2130 ). For example, processor  1715  may transmit the target trajectory from vehicle  1205  to server  1230 . Server  1230  may transmit the target trajectory received from vehicle  1205  to other vehicles (which may be autonomous vehicles or traditional, human-operated vehicles). Other vehicles may change their lanes or paths based on the target trajectory. In some embodiments, process  2100  may include overriding a change in trajectory that is suggested by the server. For example, when the vehicle is approaching a lane split, and the server determines to change the current lane to a lane that has been temporarily closed or marked for other traffic, processor  1715  may override the determination by the server based on the detection (e.g., from images captured by the camera onboard the vehicle) of the temporary closure. 
     Process  2100  may include other operations or steps. For example, processor  1715  may receive target trajectories from server  1230 . The target trajectories may be transmitted to server  1230  from other vehicles traveling ahead of vehicle  1205  on the same road segment  1200 . Processor  1715  may update an autonomous vehicle road navigation model provided in navigation system  1700  with the target trajectories received from server  1230 , and cause vehicle  1205  to make a navigational maneuver, such as changing a lane. 
     Landmark Identification 
     Consistent with disclosed embodiments, the system may identify landmarks for use in an autonomous vehicle road navigation model. This identification may include a determination of a landmark type, physical size, and location of the identified landmark, among other characteristics. 
       FIG. 22  illustrates an example environment including a system for identifying a landmark for use in autonomous vehicle navigation. In this example,  FIG. 22  shows a road segment  2200 . Vehicles  2201  and  2202  may be traveling along road segment  2200 . Along the road segment  2200 , there may be one or more signs or objects (e.g.,  2205  and  2206 ), which may be identified as landmarks. Landmarks may be stored in an autonomous vehicle road navigation model or a sparse map (e.g., sparse map  800 ). Actual images of the landmarks need not be saved in the model or sparse map. Rather, as previously discussed, a small amount of data that characterizes the landmark type, location, physical size, and, in certain cases, a condensed image signature may be stored in the model or sparse map, thereby reducing the storage space required for storing the model or sparse map and/or transmitting some or all of the sparse map to autonomous vehicles. In addition, not every landmark appearing along a road segment is stored. The model or sparse map may have sparse recording of recognized landmarks, which may be spaced apart from each other along a road segment by at least 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers, etc. Sparse recording of the landmarks also reduce the storage space required for storing data relating to the landmarks. Landmarks stored in the model and/or sparse map may be used for autonomous vehicle navigation along road segment  2200 . For example, recognized landmarks included in sparse data map  800  may be used for locating vehicles  2201  and  2202  (e.g., determining locations of vehicles  2201  and  2202  along a target trajectory stored in the model or sparse map). 
     Vehicles  2201  and  2202  may be autonomous vehicles, and may be similar to vehicles disclosed in other embodiments. For example, vehicles  2201  and  2202  may include components and devices included in vehicle  200 , such as at least one image capture device (e.g., image capture device or camera  122 ). Vehicles  2201  and  2202  may each include at least one processor  2210 , which may be similar to processor  180 ,  190 , or processing unit  110 . Each of vehicles  2201  and  2202  may include a communication unit  2215 , which may communicate with a server  2230  via one or more networks (e.g., over a cellular network and/or the Internet, etc.). 
     Server  2230  may include both hardware components (e.g., circuits, switches, network cards) and software components (e.g., communication protocols, computer-readable instructions or codes). For example, server  2230  may include a communication unit  2231  configured to communicate with communication units  2215  of vehicles  2201  and  2202 . Server  2230  may include at least one processor  2232  configured to process data, such as the autonomous vehicle road navigation model, the sparse map (e.g., sparse map  800 ), and/or navigation information received from vehicles  2201  and  2202 . The navigation information may include any information received from vehicles  2201  and  2202 , such as images of landmarks, landmark identifiers, Global Positioning System signals, ego motion data, speed, acceleration, road geometry (e.g., road profile, lane structure, elevation of road segment  2200 ), etc. Server  2230  may include a storage device  2233 , which may be a hard drive, a compact disc, a memory, or other non-transitory computer readable media. 
     Vehicles  2201  and  2202  may capture at least one image, via camera  122 , of an environment of vehicles as the vehicles travel along road segment  2200 . The image of the environment may include an image of signs or landmarks  2205  and  2206 . In some embodiments, at least one identifier associated with landmarks  2205  and  2206  may be determined by vehicles  2201  and  2202 , and the identifier may be transmitted to server  2230  from the vehicles. In some embodiments, at least one identifier associated with landmarks  2205  and  2206  may be determined by server  2230  based on images of the landmarks  2205  and  2206  captured by cameras  122  and transmitted to server  2230 . 
     For example, camera  122  installed on a host vehicle (e.g., vehicle  2201  hosting camera  122 ) may acquire at least one image representative of an environment of vehicle  2201  (e.g., in front of vehicle  2201 ). Processor  2215  included in vehicle  2201  may analyze the at least one image to identify a landmark (e.g., landmark  2206 ) in the environment of the host vehicle. Processor  2215  may also analyze the at least one image to determine the at least one identifier associated with the landmark. 
     In some embodiments, processor  2215  may then transmit the at least one identifier to server  2230 . Server  2230  (e.g., through processor  2232  and communication unit  2231 ) may receive the at least one identifier associated with the landmark. Processor  2232  may associate the landmark with the corresponding road segment  2200 . Processor  2232  may update the autonomous vehicle road navigation model relative to the corresponding road segment  2200  to include the at least one identifier associated with the landmark. Processor  2232  may distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles, such as vehicles  2201  and  2202 , and other vehicles that travel along road segment  2200  at later times. 
     The at least one identifier associated with the landmark (e.g., landmark  2205  or  2206 ) may include a position of the landmark. The position may be determined based on the signals provided by various sensors or devices installed on vehicles  2201  and  2202  (e.g., GPS signals, vehicle motion signals). The identifier may include a shape of the landmark. For example, the identifier may include data indicating a rectangular shape of landmark  2205  or a triangular shape of landmark  2206 . The identifier may include a size of the landmark. For example, the identifier may include data indicating a width and/or height of the rectangular sign  2205  and/or the triangular sign  2206 . The identifier may include a distance of the landmark relative to another landmark. For example, the identifier associated with landmark  2206  may include a distance d from landmark  2206  to landmark  2205 . The distance d is shown as a distance between landmarks  2205  and  2206  along road segment  2200 . Other distances may also be used, such as the direct distance between landmarks  2205  and  2206  crossing the road segment  2200 . In some embodiments, the distance may refer to a distance from the recognized landmark (e.g.,  2206 ) to a previously recognized landmark (e.g., a landmark that is recognized at least 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers away back along road segment  2200 ). 
     In some embodiments, the identifier may be determined based on the landmark being identified as one of a plurality of landmark types. In other words, the identifier may be the type of the landmark. The landmark types include a traffic sign (e.g., a speed limit sign), a post (e.g., a lamppost), a directional indicator (e.g., a high way exit sign with an arrow indicating a direction), a business sign (e.g., a rectangular sign such as sign  2205 ), a reflector (e.g., a reflective mirror at a curve for safety purposes), a distance marker, etc. Each type may be associated with a unique tag (e.g., a numerical value, a text value, etc.), which requires little data storage (e.g., 4 bytes, 8 bytes, etc.). When a landmark is recognized as a specific, stored type, the tag corresponding to the type of the landmark may be stored, along with other features of the landmark (e.g., size, shape, location, etc.). 
     Landmarks may be classified into two categories: landmarks that are directly relevant to driving, and landmarks that are not directly relevant to driving. Landmarks directly relevant to driving may include traffic signs, arrows on the road, lane markings, traffic lights, stop lines, etc. These landmarks may include a standard form. Landmarks directly relevant to driving may be readily recognizable by the autonomous vehicle as a certain type. Thus, a tag corresponding to the type of landmarks may be stored with small data storage space (e.g., 1 byte, 2 bytes, 4 bytes, 8 bytes, etc.). For example, a tag having a numerical value of “55” may be associated with a stop sign, “100” associated with a speed limit, “108” associated with a traffic light, etc. 
     Landmarks not directly relevant to driving may include, for example, lampposts, directional signs, businesses signs or billboards (e.g., for advertisements). These landmarks may not have a standard form. Landmarks that are not directly relevant to driving, such as billboards for advertisement and lamppost, may not be readily recognizable by the autonomous vehicle. Signs like billboards may be referred to as general signs. General signs may be identified using a condensed signature representation (or a condensed signature). For example, the identifier associated with a general sign landmark may include the condensed signature representation derived from an image of the landmark. The general sign landmark may be stored using data representing the condensed signature, rather than an actual image of the landmark. The condensed signature may require small data storage space. In some embodiments, the condensed signature may be represented by one or more integer numbers, which may require only a few bytes of data storage. The condensed signature representation may include unique features, patterns, or characteristics extracted or derived from an image of the landmarks. The condensed signature representation of landmarks may indicate an appearance of the landmarks. 
     The identifier of the landmarks may be stored within the autonomous vehicle road navigation model or sparse map  800 , which may be used for providing navigation guidance to autonomous vehicles. For example, when another vehicle later travels along road segment  2200  a previously determined position for the recognized landmark may be used in a determination of the location of that vehicle relative to a target trajectory for a road segment. 
       FIG. 23  illustrates an example environment including a system for identifying a landmark for use in autonomous vehicle navigation. Vehicles  2301  and  2302  (which may be autonomous vehicles) may travel on road segment  2200 . Vehicles  2301  and  2302  may be similar to other vehicles (e.g., vehicles  200 ,  2201 , and  2202 ) disclosed in other embodiments. Vehicle  2301  may include a camera  122 , a processor  2310 , and a communication unit  2315 . Vehicle  2302  may include a camera  122 , a processor  2311 , and a communication unit  2315 . In this embodiment, one of the vehicles  2301  and  2302  may function as a hub vehicle (e.g., vehicle  2301 ), which may perform functions performed by server  2230  in the embodiments shown in  FIG. 22 . For example, a server similar to server  2230  may be installed on hub vehicle  2301  to perform functions similar to those performed by server  2230 . As another example, the processor  2310  provided on vehicle  2301  may perform some or all of the functions of server  2230 . 
     As shown in  FIG. 23 , vehicles  2301  and  2302  may communicate with each other through communication units  2315 ,  2316 , and a communication path  2340 . Other autonomous vehicles on road segment  2200 , although not shown in  FIG. 23 , may also communicate with hub vehicle  2301 . Vehicle  2302  (and other vehicles) may transmit landmark data (e.g., images of a landmark  2206 ) captured or processed by processor  2311  on vehicle  2302  to processor  2310  on hub vehicle  2301 . Vehicle  2302  may also transmit other navigation information (e.g., road geometry) to hub vehicle  2301 . In some embodiments, processor  2310  on hub vehicle  2301  may process the landmark data received from vehicle  2302  to determine an identifier associated with a landmark detected by vehicle  2302 . In some embodiments, processor  2311  on vehicle  2302  may process images to determine an identifier associated with a landmark, and transmit the identifier to vehicle  2301 . Processor  2310  on hub vehicle  2301  may associate the landmark with road segment  2200 , and update an autonomous vehicle road navigation model and/or sparse map  800  to include the identifier associated with the landmark  2206 . Processor  2310  on hub vehicle  2301  may distribute the updated autonomous vehicle road navigation model and/or sparse map  800  to a plurality of autonomous vehicles, such as vehicle  2302  and other autonomous vehicles traveling on road segment  2200 . It should be understood that any functions referenced or described relative to the hub vehicle may be performed by one or more servers located remotely with respect to the vehicles traveling on a system of roads. For example, such servers may be located in one or more central facilities and may be in communication with deployed vehicles via wireless communication interfaces. 
       FIG. 24  illustrates a method of determining a condensed signature representation of a landmark. The condensed signature representation (or condense signature, or signature) may be determined for a landmark that is not directly relevant to driving, such as a general sign. For example, condensed signature representation may be determined for a rectangular business sign (advertisement), such as sign or landmark  2205 . The condensed signature, rather than an actual image of the general sign may be stored within the model or sparse map, which may be used for later comparison with a condensed signature derived by other vehicles. In the embodiment shown in  FIG. 24 , an image of the landmark  2205  may be mapped to a sequence of numbers of a predetermined data size, such as 32 bytes (or any other size, such as 16 bytes, 64 bytes, etc.). The mapping may be performed through a mapping function indicated by arrow  2405 . Any suitable mapping function may be used. In some embodiments, a neural network may be used to learn the mapping function based on a plurality of training images.  FIG. 24  shows an example array  2410  including 32 numbers within a range of −128 to 127. The array  2410  of numbers may be an example condensed signature representation or identifier of landmark  2205 . 
       FIG. 25  illustrates another method of determining a condensed signature representation of a landmark. For example, a color pattern may be extracted or derived from an image of a general sign, such as rectangular business sign  2205 . As another example, a brightness pattern may be extracted or derived from the image of the general sign. The condensed signature representation may include at least one of the color pattern or the brightness pattern. In some embodiments, an image of the landmark  2205  may be divided into a plurality of pixel sections, as shown by the grids in  FIG. 25 . For each pixel section, a color value or a brightness value may be calculated and associated with the pixel section, as represented by one of the circle, star, or triangle. A pattern  2500  may represent a color pattern (in which case each of the circle, start, and triangle represents a color value), or a brightness pattern (in which case each of the circle, start, and triangle represents a brightness value). Pattern  2500  may be used as the condensed signature representation of landmark  2205 . 
       FIG. 26  illustrates an example block diagram of a memory, which may store computer code or instructions for performing one or more operations for identifying a landmark for use in autonomous vehicle navigation. As shown in  FIG. 26 , memory  2600  may store one or more modules for performing the operations for identifying a landmark for use in autonomous vehicle navigation. 
     For example, memory  2600  may include a model updating and distribution module  2605  and a landmark identifier determination module  2610 . In some embodiments, the model updating and distribution module  2605  and the landmark identifier determination module  2610  may be stored in the same memory  2600 , or in different memories. A processor may execute the modules to perform various functions defined by the instructions or codes included within the modules. For example, when executed by a processor, the model updating and distribution module  2605  may cause the processor to update an autonomous vehicle road navigation model relative to a corresponding road segment to include at least one identifier associated with a landmark. The model updating and distribution module  2605  may also cause the processor to distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles for providing autonomous navigation. When executed by a processor, the landmark identifier determination module  2610  may cause the processor to analyze at least one image representative of an environment of a vehicle to identify a landmark in the image. The landmark identifier determination module  2610  may also cause the processor to analyze the image to determine at least one identifier associated with the landmark. The identifier may be used for updating the model in the model updating and distribution module  2605 . 
     In some embodiments, the landmark identifier determination module  2610  may be configured with a certain predefined detection priority. For example, the landmark identifier determination module  2610  may cause the processor to first search for road signs, and if no road sign is found within a certain distance from a previous landmark, then landmark identifier determination module  2610  may use other landmarks. 
     In addition the landmark identifier determination module  2610  may include a minimum landmark density/frequency and a maximum landmark density/frequency, to limit the landmark frequency (e.g., detected or stored landmarks over a predetermined distance). In some embodiments, these limits may ensure that there are enough landmarks but not too many that are recognized or detected and stored. 
     In some embodiments, the landmark density/frequency may be associated with a storage size or a bandwidth size. When more road signs are available, more storage space or bandwidth may be used. Alternatively or additionally, different settings may be associated with different types of landmarks. For example, traffic signs may be associated with a higher landmark density/frequency, whereas general signs may be associated with a lower landmark density/frequency, such that within a predetermined distance, more traffic signs may be detected and stored than general signs. 
     In some embodiments, memory  2600  may be included in server  2230 , for example, as part of storage device  2233 . Processor  2232  included in server  2230  may execute the model updating and distribution module  2605  to update the autonomous vehicle road navigation model to include at least one identifier associated with a landmark, and distribute the updated model to a plurality of autonomous vehicles. In some embodiments, processor  2232  included in server  2230  may receive data (images of landmarks, navigation information, road information, etc.) from vehicles (e.g.,  2201 ,  2202 ), and may execute the landmark identifier determination module  2610  to determine an identifier associated with the landmark based on the received data. 
     In some embodiments, memory  2600  may be a memory provided on a hub autonomous vehicle that performs functions of server  2230 . For example, when the hub vehicle is vehicle  2201 , processor  2210  may execute the model updating and distribution module  2605  to update the autonomous vehicle road navigation model to include an identifier associated with a landmark. Processor  2210  may also distribute the updated model to a plurality of other autonomous vehicles traveling on road segment  2200 . In some embodiments, processor  2210  of hub vehicle  2201  may receive data (e.g., images of landmarks, navigation information, road information, etc.) from other autonomous vehicles (e.g., vehicle  2202 ). Processor  2210  of hub vehicle  2201  may execute the landmark identifier determination module  2610  to determine an identifier of a landmark based on the data received from other autonomous vehicles. For example, processor  2210  of hub vehicle  2201  may analyze an image of an environment of another vehicle to identify a landmark, and to determine at least one identifier associated with the landmark. The identifier may be used by the model updating and distribution module  2605  in updating the model by hub vehicle  2201 . 
     In some embodiments, the model updating and distribution module  2605  and the landmark identifier determination module  2610  may be stored in separate memories. For example, the model updating and distribution module  2605  may be stored in a memory included in server  2230 , and the landmark identifier determination module  2610  may be stored in a memory provided on an autonomous vehicle (e.g., a memory of a navigation system provided on vehicles  2201 ,  2202 ,  2301 , and  2302 ). A processor provided in server  2230  (e.g., processor  2232 ) may execute the model updating and distribution module  2605  to update the model and distribute the updated model to autonomous vehicles. A processor provided in the autonomous vehicles (e.g., processor  2210 ,  2310 , or  2311 ) may execute the landmark identifier determination module  2610  to determine an identifier associated with a landmark. 
       FIG. 27  is a flowchart showing an exemplary process  2700  for determining an identifier of a landmark. Process  2700  may be performed when the landmark identifier determination module  2610  is executed by a processor, e.g., processor  2232  included in server  2230 , or processor  2210 ,  2310 , and  2311  provided on autonomous vehicles. Process  2700  may include acquiring at least one image of an environment of a host vehicle (step  2710 ). For example, camera  122  provided on host vehicle  2202  (on which camera  122  is hosted) may capture at least one image of the environment surrounding vehicle  2202 . Processor  2210  provided on vehicle  2202  may receive the image from camera  122 . Process  2700  may also include analyzing the image to identify a landmark (step  2720 ). For example, processor  2210  provided on vehicle  2202  may analyze the image received from camera  122  to identify a landmark in the environment surrounding vehicle  2202 . Process  2700  may also include analyzing the image to determine at least one identifier associated with the landmark (step  2730 ). For example, processor  2210  provided on vehicle  2202  may analyze the image received from camera  122  to determine at least one identifier associated with the landmark. The identifier may include any observable characteristic associated with the candidate landmark, including any of those discussed above, among others. For example, observation of such landmarks may be made through visual recognition based on analysis of captured images and/or may involve sensing by one of more sensors (e.g., a suspension sensor), or any other means of observation. 
     Process  2700  may include other operations or steps. For example, in identifying a landmark from the image of the environment, processor  2210  may identify the landmark based on a predetermined type. In determining the identifier associated with the landmark, processor  2210  may determine a position of the landmark based on GPS signals received by vehicle  2202 , or other sensor signals that may be used to determine the position. Processor  2210  may determine at least one of a shape or size of the landmark from the image. Processor  2210  may also determine a distance of the landmark to another landmark as appearing in the image, or in the real world. Processor  2210  may extract or derive a condensed signature representation as part of the identifier of the landmark. Processor  2210  may determine the condensed signature representation based on mapping the image of the landmark to a sequence of numbers of a predetermined data size (e.g., 32 bytes, 64 byte, etc.). Processor  2210  may determine at least one of a color pattern or a brightness pattern as the condensed signature representation of the landmark. 
       FIG. 28  is a flowchart showing an exemplary process for updating and distributing a vehicle road navigation model based on an identifier. Process  2800  may be performed when the model updating and distribution module  2602  is executed by a processor, such as processor  2232  included in server  2230 , or processors  2210 ,  2310 , and  2311  included in autonomous vehicles. Process  2800  may include receiving an identifier associated with a landmark (step  2810 ). For example, processor  2232  may receive at least one identifier associated with a landmark from autonomous vehicle  2201  or  2202 . Process  2800  may include associating the landmark with a corresponding road segment (step  2820 ). For example, processor  2232  may associate landmark  2206  with road segment  2200 . Process  2800  may include updating an autonomous vehicle road navigation model to include the identifier associated with the landmark (step  2830 ). For example, processor  2232  may update the autonomous vehicle road navigation model to include an identifier (including, e.g., position information, size, shape, pattern) associated with landmark  2205  in the model. In some embodiments, processor  2232  may also update sparse map  800  to include the identifier associated with landmark  2205 . Process  2800  may include distributing the updated model to a plurality of autonomous vehicles (step  2840 ). For example, processor  2232  may distribute the updated model to autonomous vehicles  2201 ,  2202 , and other vehicles that travel on road segment  2200  at later times. The update model may provide updated navigation guidance to autonomous vehicles. 
     Process  2800  may include other operations or steps. For example, processor  2232  included in server  2230  may perform some or all of the process  2700  for determining an identifier associated with a landmark. Processor  2232  may receive data (including an image of the environment) related to landmarks from vehicles  2201  and  2202 . Processor  2232  may analyze the data (e.g., the image) to identify a landmark in the image and to determine an identifier associated with the image. 
     The disclosed systems and methods may include other features. For example, in some embodiments, the vehicle location along a road segment may be determined by a processor on the vehicle or a remote server by integrating the velocity of the vehicle between two landmarks. Thus, the landmarks may serve as one dimensional (1D) localization anchors. In the model, the position of a landmark may be computed based on positions identified by multiple vehicles in multiple drives by, e.g., averaging these positions. 
     For certain landmarks, such as the general signs, the disclosed systems store an image signature (e.g., a condensed signature) rather than an actual image of the landmarks. Some types of landmarks may be detected with a relatively high precision, and may be readily used for localization (e.g., determining the position of the vehicle). For example, a sign that is directly relevant to traffic, such as a circular speed limit sign with the digits “80” may be readily classified as a certain type and easily detected. On the other hand, a beacon sign (e.g., a rectangular advertisement sign) that invites the driver to a nearby restaurant may be harder to find without any false detections. The reason is that it is difficult to learn a model for a very diverse class of objects (e.g., the advertisement sign may not fall into a known class or type). When other easy-to-detect signs are not available, general signs may also be used as landmarks, although they pose some risk of false detections. 
     For a landmark that is hard to interpret, the disclosed systems associate an appearance signature (or condensed signature, or signature) with it. The signature may be stored in the model (e.g., the road model or the autonomous vehicle road navigation model), together with the positional information of the landmark. When the vehicle detects such an object and matches it to the stored mode, the disclosed systems may match the signatures of the landmarks. The signature may not encode class information (e.g., class information indicating whether the identified object is a sign), but rather a “same-not-same” information (e.g., information indicating whether the identified object is the same as one that has been before, or one that has been stored in the model. 
     The systems (e.g., the remove server or the processor provided on the vehicle) may learn the image signatures from prior examples. A pair of images may be tagged the “same” if and only if they belong to the same specific object (a particular landmark at a particular position). In some embodiments, the disclosed systems may learn the signatures using a neural network, such as a Siamese neural network. The signature of the landmark may require small storage space, such as 10 bytes, 20 bytes, 32 bytes, 50 bytes, 100 bytes, etc. 
     The landmarks may be used for longitudinal localization of a vehicle along a road. Once the relative distances between landmarks (e.g., a first landmark and a second landmark spaced apart from the first landmark by a certain distance along the road) are estimated with a sufficient accuracy, then once when the vehicle passes a landmark, the vehicle may “reset” the identifier position estimation and cancel errors that emerge from integration of ego speed. 
     The system may use the ego speed from either the wheels sensor (e.g., a speed sensor), or from the Global Navigation Satellite System (GNSS) system. In the first option, the system may learn a calibration factor per vehicle, to cancel inter-vehicles variability. 
     To localize the position of a camera, the disclosed systems may identify visual landmarks. Any object with prominent features that may be repeatedly identified may serve as a visual landmark. On road scenarios, road side signs and traffic signs in particular, frequently serve as landmarks. Traffic signs usually are associated with a type. Traffic sign of “yield” type, for example, may appear exactly or substantially the same all over a particular country. When the disclosed systems identify a traffic sign with a type, also known as typed-traffic-sign, the systems may look for this type in the map and establish the camera localization when a match is found. 
     Some traffic signs, however, do not look the same. A common example is the “directional” traffic signs, which tell the driver which lane goes where. Other, more generic, signs may also be used, such as signs of a particular restaurant or advertisements. The standard traffic signs may be detected using traffic sign recognition algorithms designed to recognize tens or a few hundreds of signs. These standard signs may be stored in the map using one identification byte and a few bytes for localization (e.g., position of the signs). 
     One way to store generic signs is to store the image of the signs in the map database, and look for that image. This solution, however, may require a large memory footprint. Whereas a typed traffic sign may require a single integer (e.g., four bytes), an image patch with an untyped-traffic sign may require 256 bytes or more to store even a low resolution of 32×32 pixel image. The solution provided by the disclosed systems and methods uses a signature function that maps any given image patch showing the sign to a unique sequence of 32 bytes (any other bytes may also be used, e.g., 50 bytes). Conceptually, the output of the function is the signature of the image patch showing the sign. Using this signature function, the systems may transform any sign to a “signature,” which may be a sequence of 32 bytes (or 8 integers). Using the signature, the system may then look in the map for the location of a sign with a similar signature or conversely, look in the image for a signature, which according to the map, should be visible in that area of the image. 
     The signature function may be designed to give similar signatures to similar image patches, and different signatures to different image patches. The systems may use a deep neural network to learn both the signature function and a distance function between two signatures. In the neural network, the actual size of the sign in the image is not known. Rectangles of various sizes that may be candidates for signs are detected in the image. Each rectangle may then be scaled to a uniform size of, for example, 32×32 pixels, although other sizes may also be used. For training the neural network, similar images of the same sign are tagged as the “same,” whereas images for different signs captured in the same geographic location are tagged as “different.” The image patches were all scaled to a uniform size. The systems may use a Siamese network that receives two image patches of 32×32 pixels each and outputs a binary bit: 0 means image patches are not the same and 1 means image patches are the same. For example, in the example shown in  FIG. 24 , the signature  2410  of landmark  2205  may be stored in the map. The signature includes a sequence of integer numbers (first sequence), as shown in  FIG. 24 . When a vehicle passes a sign at the same location as sign  2205 , the vehicle may capture an image of the sign, and derive a second sequence of numbers using the mapping function. For example, the second sequence of number may include [44, 4, −2, −34, −58, 5, −17, −106, 26, −23, −8, −4, 7, 57, −16, −9, −11, 1, −26, 34, 18, −21, −19, −52, 17, −6, 4, 33, −6, 9, −7, −6]. The system may compare the second sequence with the first second sequence using the neural network, which may output a score for the comparison. In some embodiments, a negative score may indicate that the two signatures are not the same, and a positive score may indicate that the two signatures are the same. It is noted that the system may not require two signatures to be exactly the same in order for them to be regarded as the same. The neural network may be capable of processing low resolution input images, which leads to a low computational cost while achieving high performance. 
     After the training is completed, the Siamese network may be separated into two networks: Signature network, which may be the part of the network that receives a single patch, and outputs the “signature” of the landmark image; and the SNS (same-not-same) network, which may be the part that receives the two different signatures and outputs a scalar (e.g., the score). 
     The signature of a landmark may be attached to its location on the map. When a rectangle candidate for landmark is observed, its signature may be computed using the Signature-network. Then the two signatures, the one from the map and the one from the current landmark are fed into the SNS network. If the output score of the SNS network is negative, it may indicate that the landmark in the captured image is not the same as the one stored in the map. If the output score of the SNS network is positive, it may indicate that the landmark in the captured image is the same as the one stored in the map. 
     The signatures may require small storage space. For example, the signatures may use 32 bytes (although other size, such as 10 bytes, 20 bytes, 50 bytes, etc., may also be used). Such small-sized signatures may also enable transmission on low bandwidth communication channels. 
     Signatures may be associated with other sizes. There may be a tradeoff between the length (hence the size) of the signature and the discrimination ability of the algorithm. Smaller size may give a higher error rate, whereas larger signature may give less error. Since the disclosed systems may limit the discrimination requirements to landmark signatures from the same geographic location, the signature size may be more compact. 
     An example use of landmarks in an autonomous navigation system included in a vehicle is provided below. A camera provided on the vehicle may detect a landmark candidate, e.g., a rectangular sign. A processor (provided on the vehicle or on a remote server) may scale the rectangular sign to a standard size (for example 32×32 pixels). The processor may compute a signature (for example using a system, such as a neural network trained on example data). The processor may compare the computed signature to a signature stored in the map. If signatures match then the processor may obtain the size of the landmark size from the map. The processor may also estimate a distance from the vehicle to the landmark based on the landmark size and/or vehicle motion data (e.g., speed, translation and/or rotation data). The processor may use the distance from the landmark to localize the position of the vehicle along the road or path (e.g., along a target trajectory stored in the map). 
     The disclosed systems and methods may detect typical street structures such as lampposts. The system may take into account both the local shape of the lamppost and the arrangement of the lamppost in the scene: lampposts are typically at the side of the road (or on the divider), lampposts often appear more than once in a single image and at different sizes, Lampposts on highways may have fixed spacing based on country standards (e.g., around 25 m to 50 m spacing). The disclosed systems may use a convolutional neural network algorithm to classify a constant strip from the image (e.g., 136×72 pixels) that may be sufficient to catch almost all the street poles. The network may not contain any affine layers, and may only be composed of convolution layers, Max Pooling vertical layers and ReLu layers. The network&#39;s output dimension may be 3 times of the strip width, these three channels may have 3 degrees of freedom for each column in the strip. The first degree of freedom may indicate whether there is a street pole in this column, the second degree of freedom may indicate this pole&#39;s top, and the third degree of freedom may indicate its bottom. With the network&#39;s output results, the system may take all the local maximums that are above a threshold, and built rectangles bounding the poles. 
     After the system obtains the initial rectangles, the system may use two alignment neural networks and one filter neural network, and algorithms to track these poles including optical flow and Kalman Filter. Poles that were detected multiple times and tracked well were given a higher confidence. 
     This disclosure introduces an idea related to landmark definition within the context of camera (or vehicle) localization in urban scenarios. Some landmarks such as traffic signs tend to be quite common and one single landmark of this sort may not be uniquely identified unless the GPS localization is good. However a sequence of common landmarks may be quite unique and may give localization even when GPS signal is poor as in “urban canyons.” An urban area with high buildings may cause satellites signal to reflect, hence causing poor GPS signals. On the other hand, an urban area is crowded with landmarks of all kinds and sorts. Hence a camera may be used to self-localize, using visual landmarks. Certain landmarks, however, may be seen repeatedly along the path or trajectory, making it hard to match the landmark to a concrete location on the map. A “yield” sign may be common in urban scenarios. When observing just a “yield” traffic sign, the system may not be able to use it for localization, since there are many “yield” signs in the vicinity of the vehicle, and the system may not be able to know which one of them is the one captured by the camera. 
     The disclosed systems and methods may use any of the following solutions. In solution one, while virtually all localization algorithms use landmarks, the disclosed system may use the positional arrangement of the landmarks to create a positional-landmark. For example, the positional relation between a plurality of landmarks appearing in the same image may be measured by a vehicle. The configuration of the landmarks&#39; positional relation may be taken as a positional-landmark. Instead of just noting the landmarks, the system may compute also the distances among the different landmarks appearing in the same image. These set of distances may establish a signature of the landmark positioning with respect to each other. For example, a sequence of landmarks detected by the vehicle may be spaced apart by 11 mm between the first and the second landmarks, 16 mm between the second and third landmarks, and 28 mm between the third and fourth landmarks. In some embodiments, the specific positional arrangement of the currently visible landmarks may be unique in the map, and therefore, may be used as a positional-landmark for localization purposes. Since the positional-landmarks may be unique, it may be easy to associate them with a location on the map. 
     Solution two uses another way to create a unique landmark based on landmarks is to use the sequence of the landmarks, rather than a single landmark. For example, a sequence of landmarks may include a stop sign, a speed limit sign, and a yield sign. While the yield sign landmark may be abundant, and hence have little localization value, the sequence of several landmarks may be more unique and may lead to unambiguous localization. 
     In some embodiments, the above solutions may be used together. For example, the route may be tagged with a sequence of landmarks and the distance between them. When the GPS signal is weak, the location and distance between landmarks may be based primarily on odometry (e.g., based on images and/or inertial sensors and speedometer). Multiple vehicles drive along the route and capture landmarks and their positions along the route. The collected information regarding the landmarks may be sent from the vehicles to the server. The server may collate the landmark information into landmarks sequences. Each data collecting vehicles may give slightly different distances. The average distance or a robust statistic such as median may be used. The variance among the distances may also be stored in the map. The sequences of the landmarks may be aligned taking into account possibly missing landmarks in the recordings from some of the vehicles. The number of times a landmark is missing gives an indication as to the landmark visibility. The visibility of the landmark at that position in the sequence may also be stored in the map. 
     When a client vehicle drives along the route, it may compare the landmarks and distances detected by the client vehicle with the sequences stored in the map (or alternatively received from the server). The vehicle may match landmarks types in both sequences, and may penalize the detected sequence for missing landmarks and for distance errors. Landmarks that have low visibility or that have large distance variances may be penalized less. 
     The camera for detecting landmarks may be augmented any distance measurement apparatus, such as laser or radar. 
     It is possible that vehicles traveling along a route may record two different sequences. For example, of 50 vehicles traveling along a route, 20 of them may report a sequence of “star, star, square, circle, star” (where “star,” “square,” “circle” may each represent a certain type of sign) with consistent distances, whereas the other 30 of them may report: “star, star, square, square, triangle” with consistent distances and where the first three “star, star, square” have consistent distance with the other 20 vehicles. This may indicate that there is some interesting road feature such as an intersection or road split. 
     Refining Landmark Positions 
     While the models used for steering in the system need not be globally accurate, consistent with disclosed embodiments, global localization may be useful for navigation systems. For example, global coordinates may be useful as an index to determine which local map may be relevant for navigation along a particular road segment or to differentiate one similar landmark from another (e.g., a speed limit sign located near milepost 45 versus a similar speed limit sign located at milepost 68). Global coordinates may be assigned to landmarks in the model by first determining, based on image analysis, a location of a particular landmark relative to a host vehicle. Adding these relative coordinates to the host vehicle&#39;s global position may define the global position of the landmark. This measurement, however, may be no more accurate than the measured position of the host vehicle based on the standard automotive Global Navigation Satellite System (GNSS) receiver. Thus, while such position determination may be sufficient for indexing purposes, the disclosed navigational techniques described in detail in later sections rely upon landmarks to determine a current position of a vehicle relative to a target trajectory for a road segment. Usage of landmarks for this purpose may require more accurate position information for the landmarks than a GPS based measurement can provide. For example, if a GPS measurement is accurate only to +−5 meters, then the position determination relative to a target trajectory could be incorrect by 5 or meters, which may be unsuitable for enabling the vehicle to follow a target trajectory. 
     One solution would be to survey the landmarks associated with a road segment and define highly accurate positions for those landmarks in global coordinates. Such a method, however, would be prohibitively costly in time and money. As another approach to refining the accuracy of a determined landmark position (to a level sufficient to serve as a global localization reference for the disclosed methods of autonomous vehicle navigation), multiple measurements of the landmark position may be made, and the multiple measurements may be used to refine the determined position of the landmark. The multiple measurements may be obtained by passing vehicles equipped to determine a position of the landmark relative to GPS positions for the vehicles obtained as the vehicles pass by the landmark. 
       FIGS. 22 and 23  each show an example system for identifying a landmark for use in autonomous vehicle navigation. The systems may also determine a location or position of the landmark. In the embodiment shown in  FIG. 22 , the system may include server  2230 , which may be configured to communicate with a plurality of vehicles (e.g., vehicles  2201  and  2202 ) traveling on road segment  2200 . Along the road segment  2200 , there may be one or more landmarks. The landmarks may include at least one of a traffic sign, an arrow, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, or a lamppost. For illustration,  FIG. 22  shows two landmarks  2205  and  2206 . Server  2230  may receive data collected by vehicles  2201  and  2202 , including landmarks (e.g.,  2205  and  2206 ) recognized by vehicles  2201  and  2202 . Data collected by vehicles  2201  and  2202  regarding landmarks may include position data (e.g., location of the landmarks), physical size of the landmarks, distances between two sequentially recognized landmarks along road segment  2200 , the distance from vehicle  2201  or  2202  to a landmark (e.g.,  2205  or  2206 ). Vehicles  2201  and  2202  may both pass landmark  2206 , and may measure positions of landmark  2206  and transmit the measured positions to server  2230 . Server  2230  may determine a refined position of a landmark based on the measured position data of the landmarks received from vehicles  2201  and  2202 . For example, the refined position may be an average of the measured position data received from vehicles  2201  and  2202 , which both pass and recognize landmark  2206 . The refined position of landmark  2206  may be stored in an autonomous vehicle road navigation model or sparse map  800 , along with an identifier (e.g., a type, size, condensed signature) of landmark  2206 . The target position of landmark  2206  may be used by other vehicles later traveling along road segment  2200  to determine their location along a target trajectory associated with road segment  2200 , which may be stored in the model or sparse map. A refined position of a recognized landmark (e.g., one that has been included in sparse map  800 ) may be updated or further refined when server  2230  receives new measured position data from other vehicles relative to the recognized landmark. 
     In the embodiment shown in  FIG. 23 , the system may utilize one of the autonomous vehicles as a hub vehicle to perform some or all of the functions performed by the remote server  2230  shown in  FIG. 22 , and therefore, may not include a server. The hub vehicle may communicate with other autonomous vehicles and may receive data from other vehicles. The hub vehicle may perform functions related to generating a road model, an update to the model, a sparse map, an update to the sparse map, a target trajectory, etc. In some embodiments, the hub vehicle may also determine a refined position of a landmark stored in a model or sparse map based on multiple positions measured by multiple vehicles traversing road segment  2200 . 
     For example, in the embodiment shown in  FIG. 23 , vehicle  2201  may be the hub vehicle, which includes at least one processor (e.g., processor  2310 ) configured to receive various data, including measured position of landmark  2206 , from vehicle  2202 . Vehicle  2201  may determine a refined position of landmark  2206  based on the measured position data received from vehicle  2202 , and other previously received measured position data from vehicles previously passed and recognized landmark  2206 . The refined position of landmark  2206  may be stored within the road model or sparse map  800 . The refined position may be updated or refined when vehicle  2201  receives new measured position data from other vehicles regarding the same landmark. 
       FIG. 29  shows an example block diagram of a system  2900  for determining/processing/storing a location of a landmark. System  2900  may be implemented in server  2230  or in a hub vehicle (e.g.,  2201 ). System  2900  may include a memory  2910 . Memory  2910  may be similar to other memories disclosed in other embodiments. For example, memory  2910  may be a non-transitory flash memory. Memory  2910  may store data such as computer codes or instructions, which may be executed by a processor. System  2900  may include a storage device  2920 . Storage device  2920  may include one or more of a hard drive, a compact disc, a magnetic tape, etc. Storage device  2920  may be configured to store data, such as sparse map  800 , an autonomous vehicle road navigation model, road profile data, landmarks information, etc. System  2900  may include at least one processor  2930  configured to execute various codes or instructions to perform one or more disclosed methods or processes. Processor  2930  may be similar to any other processors disclosed in other embodiments. Processor  2930  may include both hardware components (e.g., computing circuits) and software components (e.g., software codes). System  2900  may also include a communication unit  2940  configured to communicate with autonomous vehicles via wireless communications, such as wireless internet, cellular communications network, etc. 
       FIG. 30  shows an example block diagram of memory  2910  included in system  2900 . Memory  2910  may store computer code or instructions for performing one or more operations for determining a location or position of a landmark for use in autonomous vehicle navigation. As shown in  FIG. 30 , memory  2910  may store one or more modules for performing the operations for determining the location of a landmark. 
     For example, memory  2910  may include a landmark identification module  3010  and a landmark position determination module  3020 . Memory  2910  may also include a landmark position refining module  3030 . A processor (e.g., processor  2930 ) may execute the modules to perform various functions defined by the instructions or codes included within the modules. 
     For example, when executed by a processor, the landmark identification module  3010  may cause the processor to identify a landmark from an image captured by a camera provided on a vehicle. In some embodiments, the processor may acquire at least one environmental image associated with a host vehicle from a camera installed on the host vehicle. The processor may analyze the at least one environmental image to identify the landmark in the environment of the host vehicle. The processor may identify a type of the landmark, a physical size of the landmark, and/or a condensed signature of the landmark. 
     When executed by a processor, the landmark position determination module  3020  may cause the processor to determine a position of a landmark. In some embodiments, the processor may receive global positioning system (GPS) data representing a location of the host vehicle, analyze the environmental image to determine a relative position of the identified landmark with respect to the host vehicle (e.g., a distance from the vehicle to the landmark). The processor may further determine a globally localized position of the landmark based on at least the GPS data and the determined relative position. This globally localized position may be used as the location of the landmark and stored in the model or map. 
     When executed by a processor, the landmark position refining module  3030  may refine a position determined by module  3020 . In some embodiments, the processor may receive multiple positions relating to the same landmark from multiple vehicles in multiple drive, or may measure the positions of the same landmark by the same vehicle in multiple drives. The multiple positions may be used to refine a position of the landmark already stored in the map. For example, the processor may calculate an average of the multiple positions, or a median value of the multiple positions and use that (average or median) to update the position of the landmark stored in the map. As another example, whenever the processor receives a new position measured by a new vehicle identifying the same landmark, the new position may be used to update the position of the landmark already stored in the map. 
     Various methods may be used to determine the relative position of the identified landmark with respect to the vehicle based on the analysis of one or more images captured by a camera provided on the vehicle. For example,  FIG. 31  shows a method for determining a relative position of the landmark to the host vehicle (or a distance from the host vehicle to the landmark) based on a scale associated with one or more images of the landmark. In this example, camera  122  provided on vehicle  2201  may capture an image  3100  of the environment in front of vehicle  2201 . The environment may include landmark  3130 , which is a speed limit sign, as represented by the circle with number “70.” The focus of expansion is indicated by number  3120 . Camera  122  may capture a plurality of images of the environment, such as a sequence of images. The speed limit sign  3130  may appear in a first image at the location indicated by time t 1 . The speed limit sign  3130  may appear in a second image captured after the first image at the location indicated by time t 2 . The distance between the first location (at time t 1 ) to the focus of expansion  3120  is indicated by r, and the distance between the first and second locations of the speed limit sign  3130  is indicated by d. The distance from vehicle  2201  to landmark  3130  may be calculated by Z=V*(t 2 −t 1 )*r/d, where V is the speed of vehicle  2201 , and Z is the distance from vehicle  2201  to landmark  3130  (or the relative position from the landmark  3130  to vehicle  2201 ). 
       FIG. 32  illustrates a method for determining the relative position of the landmark with respect to the host vehicle (or a distance from the vehicle to the landmark) based on an optical flow analysis associated with a plurality of images of the environment within a field of view  3200 . For example, camera  122  may capture a plurality of images of the environment in front of vehicle  2201 . The environment may include a landmark. A first image of the landmark (represented by the smaller bold rectangle) is referenced as  3210 , and a second image of the landmark (represented by the larger bold rectangle) is referenced as  3220 . An optical flow analysis may analyze two or more images of the same object, and may derive an optical flow field  3230 , as indicated by the field of arrows. The first focus of expansion is referenced by number  3240 , and the second focus of expansion is referenced by number  3250 . In some embodiments, the optical flow analysis may determine a time to collision (TTC) based on a rate of expansion derived from the optical flow of the images. The distance from the vehicle to the landmark may be estimated based on the time to collision and the speed of the vehicle. 
       FIG. 33A  is a flowchart showing an example process  3300  for determining a location of a landmark for use in navigation of an autonomous vehicle. Process  3300  may be performed by processor  2930 , which may be included in a remote server (e.g., server  2230 ), or on an autonomous vehicle (e.g., vehicle  2301 ). Process  3300  may include receiving a measured position of a landmark (step  3310 ). For example, processor  2930  may receive a measured position of landmark  2206  from vehicle  2202 . Vehicle  2202  may measure the position of the landmark  2206  based on the GPS data indicating the location of the vehicle, a relative position of the landmark with respect to vehicle  2202  determined from analysis of one or more images of an environment of vehicle  2202  including the landmark. Process  3000  may include determining a refined position of the landmark based on the measured position and at least one previously acquired position of the landmark (step  3320 ). For example, processor  2930  may average the measured position with the at least one previously acquired position of the landmark, such as one or more previously acquired positions received from other vehicles that identified the landmark. In some embodiments, processor  2930  may average the measured position with a position stored in a map (e.g., sparse map  800 ) that is determined based on at least one previously acquired position of the landmark. Processor  2930  may use the averaged position as the refined position. In some embodiments, processor  2930  may calculate a median value of the measured position and the at least one previously acquired position (e.g., a plurality of previously acquired positions), and use the median value as the refined position. Other statistical parameters that may be obtained from the measured position and the plurality of previously acquired positions may be used as the target position. Process  3000  may update the location of the landmark stored in a map with the refined position (step  3330 ). For example, processor  2930  may replace the position stored in the map with the refined position. When new position data is received, processor  2930  may repeat steps  3320  and  3330  to refine the position of the landmark stored in the map, thereby increasing the accuracy of the position of the landmark. 
       FIG. 33B  is a flowchart showing an example process  3350  for measuring the position of a landmark. Process  3350  may be performed by processor  2930 , which may be provided in server  2230  or the autonomous vehicles (e.g., vehicles  2201 ,  2202 ,  2301 , and  2302 ). A previously acquired position stored in a map may also be obtained using process  3350 . Process  3350  may include acquiring an environmental image from a camera (step  3351 ). For example, camera  122  provided on vehicle  2202  may capture one or more images of the environment of vehicle  2202 , which may include landmark  2206 . Processor  2930  may acquire images from camera  122 . Process  3350  may include analyzing the environmental image to identify a landmark (step  3351 ). For example, processor  2930  (or processor  2210 ) provided on vehicle  2202  may analyze the images to identify landmark  2206 . Process  3350  may also include receiving GPS data from a GPS unit provided on the vehicle (step  3353 ). For example, processor  2930  may receive GPS data from the GPS unit provided on vehicle  2202 . The GPS data may represent the location of vehicle  2202  (host vehicle). Process  3350  may include analyzing the environmental image to determine a relative position of the identified landmark with respect to the vehicle (step  3354 ). For example, processor  2930  may analyze the images of the environment to determine a relative position of the identified landmark  2206  with respect to vehicle  2202  using a suitable method. In some embodiments, processor  2930  may analyze the images to determine the relative position based on a scale discussed above in connection with  FIG. 31 . In some embodiments, processor  2930  may analyze the images to determine the relative position based on an optical flow analysis of the images, as discussed above in connection with  FIG. 32 . Process  3350  may further include determining a globally localized position of the landmark based on the GPS data and the determined relative position of the landmark with respect to the vehicle (step  3355 ). For example, processor  2930  may calculate the globally localized position of the landmark by combining the position of vehicle  2202  as indicated by the GPS data and the relative position (or distance from vehicle  2202  to landmark  2206 ). The globally localized position of the landmark may be used as the measured position of the landmark. 
     The disclosed systems and methods may include other features discussed below. The disclosed system may be capable of steering an autonomous vehicle along a target trajectory without knowing the precise location of the vehicle relative to a global coordinate frame. The GPS information may have an error of greater than  10 m, so the GPS information is primarily used to index the memory in order to retrieve a landmark candidate or a relevant road tile. The global localization may be determined using the visual ego motion. In order to avoid drifts, the system may estimate the GPS location of the landmarks by combining the GPS position of the host vehicle and the relative position of the landmark to the host vehicle. The global landmark location may be refined (e.g., averaged) with location data obtained from multiple vehicles and multiple drives. The measured position or location of the landmark may behave like a random variable, and hence may be averaged to improve accuracy. The GPS signals are used primarily as a key or index to a database storing the landmarks, and do not have to have high precision for determining the position of the vehicle. Low precision GPS data may be used to determine the location of the vehicle, which is used to determine the position of the landmark. Errors introduced by the low precision GPS data may accumulate. Such errors may be fixed by averaging the position data of the landmark from multiple drives. 
     In some embodiments, for steering purpose, the GPS coordinates may only be used to index the database. The GPS data may not be taken into account in the computation of the steering angle. The model including the location of the landmarks may be transitioned to a global coordinate system. The transition may include determining the GPS coordinates of landmarks by averaging, concluding the GPS position of the vehicle near (globally localized) landmarks, and extending the global localization away from landmarks, by using the curve geometry, the location along path, the lane assignment and the in-lane position. 
     Autonomous Navigation Using a Sparse Road Model 
     In some embodiments, the disclosed systems and methods may use a sparse road model for autonomous vehicle navigation. For example, the disclosed systems and methods may provide navigation based on recognized landmarks, align a vehicle&#39;s tail for navigation, allow a vehicle to navigate road junctions, allow a vehicle to navigate using local overlapping maps, allow a vehicle to navigate using a sparse map, navigate a vehicle based on an expected landmark location, autonomously navigate a vehicle based on road signatures, navigate a vehicle forward based on a rearward facing camera, navigate a vehicle based on a free space determination, and navigate a vehicle in snow. Additionally, the disclosed embodiments provide systems and methods for autonomous vehicle speed calibration, determining a lane assignment o of a vehicle based on a recognized landmark location, and using super landmarks as navigation aids when navigating a vehicle. These systems and methods are detailed below. 
     Navigation Based on Recognized Landmarks 
     Consistent with disclosed embodiments, the system may use landmarks, for example, to determine the position of a host vehicle along a path representative of a target road model trajectory (e.g., by identifying an intersection point of a relative direction vector to the landmark with the target road model trajectory). Once this position is determined, a steering direction can be determined by comparing a heading direction to the target road model trajectory at the determined position. Landmarks may include, for example, any identifiable, fixed object in an environment of at least one road segment or any observable characteristic associated with a particular section of the road segment. In some cases, landmarks may include traffic signs (e.g., speed limit signs, hazard signs, etc.). In other cases, landmarks may include road characteristic profiles associated with a particular section of a road segment. In yet other cases, landmarks may include road profiles as sensed, for example, by a suspension sensor of the vehicle. Further examples of various types of landmarks are discussed in previous sections, and some landmark examples are shown in  FIG. 10 . 
       FIG. 34  illustrates vehicle  200  (which may be an autonomous vehicle) traveling on road segment  3400  in which the disclosed systems and methods for navigating vehicle  200  using one or more recognized landmarks  3402 ,  3404  may be used. Although,  FIG. 34  depicts vehicle  200  as equipped with image capture devices  122 ,  124 ,  126 , more or fewer image capture devices may be employed on any particular vehicle  200 . As illustrated in  FIG. 34 , road segment  3400  may be delimited by left side  3406  and right side  3408 . A predetermined road model trajectory  3410  may define a preferred path (e.g., a target road model trajectory) within road segment  3400  that vehicle  200  may follow as vehicle  200  travels along road segment  3400 . In some exemplary embodiments, predetermined road model trajectory  3410  may be located equidistant from left side  3406  and right side  3408 . It is contemplated however that predetermined road model trajectory  3410  may be located nearer to one or the other of left side  3406  and right side  3408  of road segment  3400 . Further, although  FIG. 34  illustrates one lane in road segment  3400 , it is contemplated that road segment  3400  may have any number of lanes. It is also contemplated that vehicle  200  traveling along any lane of road segment  3400  may be navigated using one or more landmarks  3402 ,  3404  according to the disclosed methods and systems. 
     Image acquisition unit  120  may be configured to acquire an image representative of an environment of vehicle  200 . For example, image acquisition unit  120  may obtain an image showing a view in front of vehicle  200  using one or more of image capture devices  122 ,  124 ,  126 . Processing unit  110  of vehicle  200  may be configured to detect one or more landmarks  3402 ,  3404  in the one or more images acquired by image acquisition unit  120 . Processing unit  110  may detect the one or more landmarks  3402 ,  3404  using one or more processes of landmark identification discussed above with reference to  FIGS. 22-28 . Although  FIG. 34  illustrates only two landmarks  3402 ,  3404 , it is contemplated that vehicle  200  may detect fewer than or more than landmarks  3402 ,  3404  based on the images acquired by image acquisition unit  120 . 
     Processing unit  110  may be configured to determine positions  3432 ,  3434  of the one or more landmarks  3402 ,  3404 , respectively, relative to a current position  3412  of vehicle  200 . Processing unit  110  may also be configured to determine a distance between current position  3412  of vehicle  200  and the one or more landmarks  3402 ,  3404 . Further, processing unit  110  may be configured to determine one or more directional indicators  3414 ,  3416  of the one or more landmarks  3402 ,  3404  relative to current position  3412  of vehicle  200 . Processing unit  110  may be configured to determine directional indicators  3414 ,  3416  as vectors originating from current position  3412  of vehicle  200  and extending towards, for example, positions  3432 ,  3434  of landmarks  3402 ,  3404 , respectively. 
     Processing unit  110  may also be configured to determine an intersection point  3418  of the one or more directional indicators  3414 ,  3416  with predetermined road model trajectory  3410 . In one exemplary embodiment as illustrated in  FIG. 34 , intersection point  3418  may coincide with current position  3412  of vehicle  200 . This may occur, for example, when vehicle  200  is located on predetermined road model trajectory  3410 . Although generally vehicle  200  may be expected to be located on or very near predetermined road model trajectory  3410 , it is contemplated that vehicle  200  may not be located on predetermined road model trajectory  3410  as will be discussed below with respect to  FIG. 35 . 
     Processing unit  110  may be configured to determine a direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 . Processing unit  110  may determine direction  3420  as a direction tangential to predetermined road model trajectory  3410 . In one exemplary embodiment, processing unit  110  may be configured to determine direction  3420  based on a gradient or slope of a three-dimensional polynomial representing predetermined road model trajectory  3410 . 
     Processing unit  110  may also be configured to determine heading direction  3430  of vehicle  200 . As illustrated in  FIG. 34 , heading direction  3430  of vehicle  200  may be a direction along which image capture device  122  may be oriented relative to a local coordinate system associated with vehicle  200 . Processing unit  110  may be configured to determine whether heading direction  3430  of vehicle  200  is aligned with (i.e., generally parallel to) direction  3420  of predetermined road model trajectory  3410 . When heading direction  3430  is not aligned with direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 , processing unit  110  may determine an autonomous steering action such that heading direction  3430  of vehicle  200  may be aligned with direction  3420  of predetermined road model trajectory  3410 . In one exemplary embodiment, an autonomous steering action may include, for example, a determination of an angle by which the steering wheel or front wheels of vehicle  200  may be turned to help ensure that heading direction  3430  of vehicle  200  may be aligned with direction  3420  of predetermined road model trajectory  3410 . In another exemplary embodiment, an autonomous steering action may also include a reduction or acceleration in a current velocity of vehicle  200  to help ensure that heading direction  3430  of vehicle  200  may be aligned with direction  3420  of predetermined road model trajectory  3410  in a predetermined amount of time. Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger a desired navigational response by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of a predetermined angle. Rotation by the predetermined angle may help align heading direction  3430  of vehicle  200  with direction  3420 . 
     Processing unit  110  may include additional considerations when determining the autonomous steering action. For example, in some exemplary embodiments, processing unit  110  may determine the autonomous steering action based on a kinematic and physical model of the vehicle, which may include the effects of a variety of possible autonomous steering actions on the vehicle or on a user of vehicle  200 . Processing unit  110  may implement a selection criteria for selecting at least one autonomous steering action from the plurality of autonomous steering actions. In other exemplary embodiments, processing unit  110  may determine an autonomous steering action based on a “look ahead” operation, which may evaluate portions of road segment  3400  located in front of current location  3418  of vehicle  200 . Processing unit  110  may determine an effect of one or more autonomous steering actions on the behavior of vehicle  200  or on a user of vehicle  200  at a location in front of current location  3418 , which may be caused by the one or more autonomous steering actions. In yet other exemplary embodiments, processing unit  110  may further account for the presence and behavior of one or more other vehicles in the vicinity of vehicle  200  and a possible (estimated) effect of one or more autonomous steering actions on such one or more other vehicles. Processing unit  110  may implement the additional considerations as overrides. Thus, for example, processing unit  110  may initially determine an autonomous steering action that may help ensure that heading direction  3430  of vehicle  200  may be aligned with direction  3420  of predetermined road model trajectory  3410  at current location  3418 . When processing unit  110  determines that the determined autonomous steering does not comply with one or more constraints imposed by the additional considerations, processing unit  110  may modify the autonomous steering action to help ensure that all the constraints may be satisfied. 
     Image acquisition unit  120  may repeatedly acquire an image of the environment in front of vehicle  200 , for example, after a predetermined amount of time. Processing unit  110  may also be configured to repeatedly detect the one or more landmarks  3402 ,  3404  in the image acquired by image acquisition unit  120  and determine the autonomous steering action as discussed above. Thus, image acquisition unit  120  and processing unit  110  may cooperate to navigate vehicle  200  along road segment  3400  using one or more landmarks  3402 ,  3404 . 
       FIG. 35  illustrates another vehicle  200  traveling on road segment  3400  in which the disclosed systems and methods for navigating vehicle  200  using one or more recognized landmarks  3402 ,  3404  may be used. Unlike  FIG. 34 , vehicle  200  of  FIG. 35  is not located on predetermined road model trajectory  3410 . As a result, as illustrated in  FIG. 35 , intersection point  3418  of directional indicator  3416  may not coincide with current position  3412  of vehicle  200 . 
     As discussed above with respect to  FIG. 34 , processing unit  110  may be configured to determine a direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 . Processing unit  110  may also be configured to determine whether heading direction  3430  of vehicle  200  is aligned with (i.e. generally parallel to) direction  3420 . When heading direction  3430  is not aligned with direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 , processing unit  110  may determine a first autonomous steering action such that heading direction  3430  of vehicle  200  may be aligned with direction  3420  of predetermined road model trajectory  3410 . For example, as illustrated in  FIG. 35 , processing unit  110  may determine the first autonomous steering action to require a rotation by an angle to help ensure that heading direction  3430  of vehicle  200  may be aligned with direction  3420 . 
     In addition, when current position  3412  of vehicle  200  is not located on predetermined road model trajectory  3410 , processing unit  120  may determine a second autonomous steering action to help ensure that vehicle  200  may move from current position  3412  to intersection point  3418  on predetermined road model trajectory  3410 . For example, as illustrated in  FIG. 35 , processing unit  110  may determine a distance “d” by which vehicle  200  must be translated to move current position  3412  to coincide with intersection point  3418  on predetermined road model trajectory  3410 . Although not illustrated in  FIG. 35 , processing unit  110  may also be configured to determine a rotation that may be required to help ensure that vehicle  200  may move from current position  3412  to intersection point  3418  on predetermined road model trajectory  3410 . Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger a desired navigational response corresponding to the first autonomous steering action, the second autonomous steering action, or some combination of the first and the second autonomous steering actions. In some embodiment, processing unit  110  may execute instructions to trigger a desired navigational response corresponding to the first autonomous steering action and the second autonomous steering action sequentially in any order. 
       FIG. 36  is a flowchart showing an exemplary process  3600 , for navigating vehicle  200  along road segment  3400 , using one or more landmarks  3402 ,  3404 , consistent with disclosed embodiments. Steps of process  3600  may be performed by one or more of processing unit  110  and image acquisition unit  120 , with or without the need to access memory  140  or  150 . The order and arrangement of steps in process  3600  is provided for purposes of illustration. As will be appreciated from this disclosure, modifications may be made to process  3600  by, for example, adding, combining, removing, and/or rearranging the steps for the process. 
     As illustrated in  FIG. 36 , process  3600  may include a step  3602  of acquiring an image representative of an environment of the vehicle. In one exemplary embodiment, image acquisition unit  120  may acquire one or more images of an area forward of vehicle  200  (or to the sides or rear of a vehicle, for example). For example, image acquisition unit  120  may obtain an image using image capture device  122  having a field of view  202 . In other exemplary embodiments, image acquisition unit  120  may acquire images from one or more of image capture devices  122 ,  124 ,  126 , having fields of view  202 ,  204 ,  206 . Image acquisition unit  120  may transmit the one or more images to processing unit  110  over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). 
     Process  3600  may also include a step  3604  of identifying one or more landmarks  3402 ,  3404  in the one or more images. Processing unit  110  may receive the one or more images from image acquisition unit  120 . Processing unit  110  may execute monocular image analysis module  402  to analyze the plurality of images at step  3604 , as described in further detail in connection with  FIGS. 5B-5D . By performing the analysis, processing unit  110  may detect a set of features within the set of images, for example, one or more landmarks  3402 ,  3404 . Landmarks  3402 ,  3404  may include one or more traffic signs, arrow markings, lane markings, dashed lane markings, traffic lights, stop lines, directional signs, reflectors, landmark beacons, lampposts, a change is spacing of lines on the road, signs for businesses, and the like. 
     In some embodiments, processing unit  110  may execute monocular image analysis module  402  to perform multi-frame analysis on the plurality of images to detect landmarks  3402 ,  3404 . For example, processing unit  110  may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit  110  may then use the 3D-map to detect the road surface, as well as landmarks  3402 ,  3404 . In another exemplary embodiment, image processor  190  of processing unit  110  may combine a plurality of images received from image acquisition unit  120  into one or more composite images. Processing unit  110  may use the composite images to detect the one or more landmarks  3402 ,  3404 . 
     In some embodiments, processing unit  110  may be able to recognize various attributes of objects that may qualify as potential landmarks. This information may be uploaded to a server, for example, remote from the vehicle. The server may process the received information and may establish a new, recognized landmark within sparse data map  800 , for example. It may also be possible for the server to update one or more characteristics (e.g., size, position, etc.) of a recognized landmark already included in sparse data map  800 . 
     In some cases, processing unit  110  may receive information from a remote server that may aid in locating recognized landmarks (e.g., those landmarks that have already been identified and represented in sparse data map  800 ). For example, as a vehicle travels along a particular road segment, processor  110  may access one or more local maps corresponding to the road segment being traversed. The local maps may be part of sparse data map  800  stored on a server located remotely with respect to the vehicle, and the one or more local maps may be wirelessly downloaded as needed. In some cases, the sparse map  800  may be stored locally with respect to the navigating vehicle. The local maps may include various features associated with a road segment. For example, the local maps may include a polynomial spline representative of a target trajectory that the vehicle should follow along the road segment. The local maps may also include representations of recognized landmarks. In some cases, as previously described, the recognized landmarks may include information such as a landmark type, position, size, distance to another landmark, or other characteristics. In the case of non-semantic signs (e.g., general signs not necessarily associated with road navigation), for example, the information stored in sparse data map  800  may include a condensed image signature associated with the non-semantic road sign. 
     Such information received from sparse data map  800  may aid processor unit  110  in identifying recognized landmarks along a road segment. For example, processor unit  110  may determine based on its current position (determined, for example, based on GPS data, dead reckoning relative to a last determined position, or any other suitable method) and information included in a local map (e.g., a localized position of the next landmark to be encountered and/or information indicating a distance from the last encountered landmark to the next landmark) that a recognized landmark should be located at a position approximately 95 meters ahead of the vehicle and 10 degrees to the right of a current heading direction. Processor unit  110  may also determine from the information in the local map that the recognized landmark is of a type corresponding to a speed limit sign and that the sign has a rectangular shape of about 2 feet wide by 3 feet tall. 
     Thus, when processor unit  110  receives images captured by the onboard camera, those images may be analyzed by searching for an object at the expected location of a recognized landmark from sparse map  800 . In the speed limit sign example, processor unit  110  may review captured images and look for a rectangular shape at a position in the image 10 degrees to the right of a heading direction of the vehicle. Further, the processor may look for a rectangular shape occupying a number of pixels of the image that a 2 foot by 3 foot rectangular sign would be expected to occupy at a relative distance of 95 meters. Upon identifying such an object in the image, where expected, the processor may develop a certain confidence level that the expected recognized landmark has been identified. Further confirmation may be obtained, for example, by analyzing the image to determine what text or graphics appear on the sign in the captured images. Through textual or graphics recognition processes, the processor unit may determine that the rectangular shape in the captured image includes the text “Speed Limit 55.” By comparing the captured text to a type code associated with the recognized landmark stored in sparse data map  800  (e.g., a type indicating that the next landmark to be encountered is a speed limit sign), this information can further verify that the observed object in the captured images is, in fact, the expected recognized landmark. 
     Process  3600  may include a step  3606  of determining a current position  3412  of vehicle  200  relative to a target trajectory. Processing unit  110  may determine current position  3412  of vehicle  200  in many different ways. For example, processing unit  110  may determine current position  3412  based on signals from position sensor  130 , for example, a GPS sensor. In another exemplary embodiment, processing unit  110  may determine current position  3412  of vehicle  200  by integrating a velocity of vehicle  200  as vehicle  200  travels along predetermined road model trajectory  3410 . For example, processing unit  110  may determine a time “t” required for vehicle  200  to travel between two locations on predetermined road model trajectory  3410 . Processing unit  110  may integrate the velocity of vehicle  200  over time t to determine current position  3412  of vehicle  200  relative to the two locations on predetermined road model trajectory  3410 . 
     Once a recognized landmark is identified in a captured image, predetermined characteristics of the recognized landmark may be used to assist a host vehicle in navigation. For example, in some embodiments, the recognized landmark may be used to determine a current position of the host vehicle. In some cases, the current position of the host vehicle may be determined relative to a target trajectory from sparse data model  800 . Knowing the current position of the vehicle relative to a target trajectory may aid in determining a steering angle needed to cause the vehicle to follow the target trajectory (for example, by comparing a heading direction to a direction of the target trajectory at the determined current position of the vehicle relative to the target trajectory). 
     A position of the vehicle relative to a target trajectory from sparse data map  800  may be determined in a variety of ways. For example, in some embodiments, a 6D Kalman filtering technique may be employed. In other embodiments, a directional indicator may be used relative to the vehicle and the recognized landmark. For example, process  3600  may also include a step  3608  of determining one or more directional indicators  3414 ,  3416  associated with the one or more landmarks  3402 ,  3404 , respectively. Processing unit  110  may determine directional indicators  3414 ,  3416  based on the relative positions  3432 ,  3434  of the one or more landmarks  3402 ,  3404 , respectively, relative to current position  3412  of vehicle  200 . For example, processing unit  110  may receive landmark positions  3432 ,  3434  for landmarks  3402 ,  3404 , respectively, from information, which may be stored in one or more databases in memory  140  or  150 . Processing unit  110  may also determine distances between current position  3412  of vehicle  200  and landmark positions  3432 ,  3434  for landmarks  3402 ,  3404 , respectively. In addition, processing unit  110  may determine directional indicator  3414  as a vector extending from current position  3412  of vehicle  200  and extending along a straight line passing through current position  3412  and landmark position  3432 . Likewise, processing unit  110  may determine directional indicator  3416  as a vector extending from current position  3412  of vehicle  200  and extending along a straight line passing through current position  3412  and landmark position  3434 . Although two landmarks  3402 ,  3404  are referenced in the above discussion, it is contemplated that processing unit  110  may determine landmark positions  3432 ,  3434 , distances between current position  3412  and landmark positions  3402 ,  34014 , and directional indicators  3414 ,  3416  for fewer than or more than landmarks  3402 ,  3404 . 
     Process  3600  may include a step  3610  of determining an intersection point  3418  of directional indicator  3416  with predetermined road model trajectory  3410 . Processing unit  110  may determine a location of intersection point  3418  at which predetermined road model trajectory  3410  intersects with a straight line extending between current position  3412  of vehicle  200  and landmark position  3434 . Processing unit  110  may obtain a mathematical representation of predetermined road model trajectory  3410  from information stored in memories  140 ,  150 . Processing unit  110  may also generate a mathematical representation of a straight line passing through both current position  3412  of vehicle  200  and landmark position  3434  of landmark  3404 . Processing unit  110  may use the mathematical representation of predetermined road model trajectory  3410  and the mathematical representation of a straight line extending between current position  3412  and landmark position  3434  to determine a location of intersection point  3418 . 
     In one exemplary embodiment as illustrated in  FIG. 34 , intersection point  3418  may coincide with current position  3412  of vehicle  200  (e.g., a position of a point of reference, which may be arbitrarily assigned, associated with the vehicle). This may happen, for example, when vehicle  200  is located on predetermined road model trajectory  3410 . In another exemplary embodiment as illustrated in  FIG. 35 , intersection point  3418  may be separated from current position  3412 . Processing unit  110  may detect that vehicle  200  is not located on predetermined road model trajectory  3410  by comparing a first distance “D 1 ” (see, e.g.,  FIG. 35 ) between current position  3412  and landmark position  3434  with a second distance “D 2 ” between intersection point  3418  and landmark position  3434 . 
     When intersection point  3418  is separated from current position  3412  of vehicle  200 , processing unit  110  may determine an amount of translation and/or rotation that may be required to help move vehicle  200  from current position  3412  to intersection point  3418  on predetermined road model trajectory  3410 . In some exemplary embodiments, processing unit  110  may execute navigation module  408  to cause one or more navigational responses in vehicle  200  based on the analysis performed at step  520  and the techniques as described above in connection with  FIG. 4 . For example, processing unit  110  may issue commands to steering system  240  to move vehicle  200  so that a current position  3412  of vehicle  200  may coincide with intersection point  3418 . 
     Process  3600  may include a step  3612  of determining direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 . In one exemplary embodiment, processing unit  110  may obtain a mathematical representation (e.g. three-dimensional polynomial) of predetermined road model trajectory  3410 . Processing unit  110  may determine direction  3420  as a vector oriented tangentially to predetermined road model trajectory  3410  at intersection point  3418 . For example, processing unit  110  may determine direction  3420  as a vector pointing along a gradient of the mathematical representation of predetermined road model trajectory  3410  at intersection point  3418 . 
     Process  3600  may also include a step  3614  of determining an autonomous steering action for vehicle  200 . In one exemplary embodiment, processing unit  110  may determine a heading direction  3430  of vehicle  200 . For example, as illustrated in  FIGS. 34 and 35 , processing unit  110  may determine heading direction  3430  of vehicle  200  as the direction in which image capture device  122  may be oriented relative to a local coordinate system associated with vehicle  200 . In another exemplary embodiment, processing unit  200  may determine heading direction  3430  as the direction of motion of vehicle  200  at current position  3412 . Processing unit  110  may also determine a rotational angle between heading direction  3430  and direction  3420  of predetermined road model trajectory  3410 . Processing unit  110  may execute the instructions in navigational module  408  to determine an autonomous steering action for vehicle  200  that may help ensure that heading direction  3430  of vehicle  200  is aligned (i.e., parallel) with direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 . Processing unit  110  may also send control signals to steering system  240  to adjust rotation of the wheels of vehicle  200  to turn vehicle  200  so that heading direction  3430  may be aligned with direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 . In one exemplary embodiment, processing unit  110  may send signals to steering system  240  to adjust rotation of the wheels of vehicle  200  to turn vehicle  200  until a difference between heading direction  3430  and direction  3420  of predetermined road model trajectory  3410  at intersection point  3418  may be less than a predetermined threshold value. 
     Processing unit  110  and/or image acquisition unit  120  may repeat steps  3602  through  3614  after a predetermined amount of time. In one exemplary embodiment, the predetermined amount of time may range between about 0.5 seconds to 1.5 seconds. By repeatedly determining intersection point  3418 , heading direction  3430 , direction  3420  of predetermined road model trajectory  3410  at intersection point  3418 , and the autonomous steering action required to align heading direction  3430  with direction  3420 , processing unit  110  and/or image acquisition unit  120  may help to navigate vehicle  200 , using the one or more landmarks  3402 ,  3404 , so that vehicle  200  may travel along road segment  3400 . 
     Tail Alignment Navigation 
     Consistent with disclosed embodiments, the system can determine a steering direction for a host vehicle by comparing and aligning a traveled trajectory of the host vehicle (the tail) with a predetermined road model trajectory at a known location along the road model trajectory. The traveled trajectory provides a vehicle heading direction at the host vehicle location, and the steering direction can be obtained, relative to the heading direction, by determining a transformation (e.g., rotation and potentially translation) that minimizes or reduces an error between the traveled trajectory and the road model trajectory at the known location of the vehicle along the road model trajectory. 
     Tail alignment is a method of aligning an autonomous vehicle&#39;s heading with a pre-existing model of the path based on information regarding the path over which the vehicle has already traveled. Tail alignment uses a tracked path of the autonomous vehicle over a certain distance (hence the “tail”). The tracked path is a representation of the path over which the autonomous vehicle has already traveled in order to reach a current location of the autonomous vehicle. For example, the tracked path may include a predetermined distance (e.g. 60 m or other desired length) of the path behind the autonomous vehicle over which the autonomous vehicle traveled to reach its current location. The tracked path may be compared with the model to determine, for example, a heading angle of the autonomous vehicle. 
     In some embodiments, a rear looking camera may be used to determine or aid in determination of the traveled path. A rear looking camera may be useful both for modeling, heading estimation, and lateral offset estimation. By adding a rear looking camera it may be possible to boost the reliability of the system, since a bad illumination situation (e.g., low sun on the horizon) rarely would affect both front looking and rear looking cameras. 
     The tracked path can also optionally be combined with a predicted path of the autonomous vehicle. The predicted path may be generated by processing images of the environment ahead of the autonomous vehicle and detecting lane, or other road layout, markings. In this regard it is worth noting, that in a potential implementation of the present disclosure, a road model may diverge due to accumulated errors (integration of ego motion). Thus, for example, a predicted path over a predetermined distance (e.g. 40 m) ahead of the current location of the autonomous vehicle may be compared with the tracked path to determine the heading angle for the autonomous vehicle. 
       FIG. 37  illustrates vehicle  200  (which may be an autonomous vehicle) traveling on road segment  3700  in which the disclosed systems and methods for navigating vehicle  200  using tail alignment may be used. As used here and throughout this disclosure, the term “autonomous vehicle” refers to vehicles capable of implementing at least one navigational change in course without driver input. To be autonomous, a vehicle need not be fully automatic (e.g., fully operational without a driver or without driver input). Rather, an autonomous vehicle includes those that can operate under driver control during certain time periods and without driver control during other time periods. Autonomous vehicles may also include vehicles that control only some aspects of vehicle navigation, such as steering (e.g., to maintain a vehicle course between vehicle lane constraints), but may leave other aspects to the driver (e.g., braking). In some cases, autonomous vehicles may handle some or all aspects of braking, speed control, and/or steering of the vehicle. 
     Although,  FIG. 37  depicts vehicle  200  as equipped with image capture devices  122 ,  124 ,  126 , more or fewer image capture devices may be employed on any particular vehicle  200 . As illustrated in  FIG. 37 , road segment  3700  may be delimited by left side  3706  and right side  3708 . A predetermined road model trajectory  3710  may define a preferred path (i.e. a target road model trajectory) within road segment  3700  that vehicle  200  may follow as vehicle  200  travels along road segment  3700 . In some exemplary embodiments, predetermined road model trajectory  3710  may be located equidistant from left side  3706  and right side  3708 . It is contemplated however that predetermined road model trajectory  3710  may be located nearer to one or the other of left side  3706  and right side  3708  of road segment  3700 . Further, although  FIG. 37  illustrates one lane in road segment  3700 , it is contemplated that road segment  3700  may have any number of lanes. It is also contemplated that vehicle  200  traveling along any lane of road segment  3400  may be navigated using tail alignment according to the disclosed methods and systems. 
     Image acquisition unit  120  may be configured to acquire a plurality of images representative of an environment of vehicle  200 , as vehicle  200  travels along road segment  3700 . For example, image acquisition unit  120  may obtain the plurality of images showing views in front of vehicle  200  using one or more of image capture devices  122 ,  124 ,  126 . Processing unit  110  of vehicle  200  may be configured to detect a location of vehicle  200  in each of the plurality of images. Processing unit  110  of vehicle  200  may also be configured to determine a traveled trajectory  3720  based on the detected locations. As used in this disclosure, the traveled trajectory  3720  may represent an actual path taken by vehicle  200  as vehicle  200  travels along road segment  3700 . 
     Processing unit  110  may be configured to determine a current location  3712  of vehicle  200  based on analysis of the plurality of images. In one exemplary embodiment as illustrated in  FIG. 37 , the current location  3712  of vehicle  200  may coincide with a target location  3714  on predetermined road model trajectory  3710 . This may occur, for example, when vehicle  200  is located on predetermined road model trajectory  3710 . Although generally vehicle  200  may be expected to be located on or very near predetermined road model trajectory  3710 , it is contemplated that vehicle  200  may not be located on predetermined road model trajectory  3710  as will be discussed below with respect to  FIG. 38 . 
     Processing unit  110  may be configured to determine an autonomous steering action for vehicle  200  by comparing the traveled trajectory  3720  with the predetermined road model trajectory  3710  at current location  3712  of vehicle  200 . For example, processing unit  110  may be configured to determine a transformation (i.e., rotation and potentially translation) such that an error between the traveled trajectory  3720  and the predetermined road model trajectory  3710  may be reduced. 
     Processing unit  110  may be configured to determine a heading direction  3730  of vehicle  200  at current location  3712 . Processing unit  110  may determine heading direction  3730  based on the traveled trajectory  3720 . For example, processing unit  110  may determine heading direction  3730  as a gradient of traveled trajectory  3720  at current location  3712  of vehicle  200 . Processing unit  110  may also be configured to determine steering direction  3740  as a direction tangential to predetermined road model trajectory  3710 . In one exemplary embodiment, processing unit  110  may be configured to determine steering direction  3740  based on a gradient of a three-dimensional polynomial representing predetermined road model trajectory  3710 . 
     Processing unit  110  may be configured to determine whether heading direction  3730  of vehicle  200  is aligned with (i.e., generally parallel to) steering direction  3740  of predetermined road model trajectory  3710 . When heading direction  3730  is not aligned with steering direction  3740  of predetermined road model trajectory  3710  at current location  3712  of vehicle  200 , processing unit  110  may determine an autonomous steering action such that heading direction  3730  of vehicle  200  may be aligned with steering direction  3740  of predetermined road model trajectory  3710 . Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger a desired navigational response by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of angle θ. Rotation by the angle θ may help align heading direction  3730  of vehicle  200  with steering direction  3740 . Thus, for example, processing unit  110  may perform tail alignment of vehicle  200  by determining the angle θ by which vehicle  200  may turn so that heading direction  3730  of autonomous vehicle may be aligned with steering direction  3740 . 
     Image acquisition unit  120  may repeatedly acquire the plurality of images of the environment in front of vehicle  200 , for example, after a predetermined amount of time. Processing unit  110  may also be configured to repeatedly determine the transformation as discussed above. Thus, image acquisition unit  120  and processing unit  110  may cooperate to navigate vehicle  200  along road segment  3400  using the traveled trajectory  3720  (i.e. the “tail”) of vehicle  200 . 
       FIG. 38  illustrates another vehicle  200  traveling on road segment  3700  in which disclosed systems and methods for navigating vehicle  200  using tail alignment. Unlike  FIG. 38 , vehicle  200  of  FIG. 38  is not located on predetermined road model trajectory  3710 . As a result, as illustrated in  FIG. 38 , target location  3714  of vehicle  200  may not coincide with current location  3712  of vehicle  200 . 
     As discussed above with respect to  FIG. 37 , processing unit  110  may be configured to determine a steering direction  3740  of predetermined road model trajectory  3710  at current location  3712  of vehicle  200 . Processing unit  110  may determine steering direction  3740  as the direction of the gradient of predetermined road model trajectory  3710  at target location  3714 . Processing unit  110  may also be configured to determine whether heading direction  3730  of vehicle  200  is aligned with (i.e., generally parallel to) steering direction  3740 . When heading direction  3730  is not aligned with steering direction  3740 , processing unit  110  may determine a transformation that may include, for example, a rotation angle that may be required to align heading direction  3730  with steering direction  3740 . In addition, the transformation may include a translation “d” that may be required to ensure that vehicle  200  may move from current location  3712  to target location  3714  on predetermined road model trajectory  3710 . 
     Processing unit  110  may be configured to determine the transformation by comparing predetermined road model trajectory  3710  with the traveled trajectory  3720  of vehicle  200 . In one exemplary embodiment, processing unit  110  may determine the transformation by reducing an error between predetermined road model trajectory  3710  and traveled trajectory  3720 . Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger a desired navigational response based on the determined transformation. 
       FIG. 39  is a flowchart showing an exemplary process  3900 , for navigating vehicle  200  along road segment  3700 , using tail alignment, consistent with disclosed embodiments. Steps of process  3900  may be performed by one or more of processing unit  110  and image acquisition unit  120 , with or without the need to access memory  140  or  150 . The order and arrangement of steps in process  3900  is provided for purposes of illustration. As will be appreciated from this disclosure, modifications may be made to process  3900  by, for example, adding, combining, removing, and/or rearranging the steps for the process. 
     As illustrated in  FIG. 39 , process  3900  may include a step  3902  of acquiring a plurality of images representative of an environment of the vehicle. In one exemplary embodiment, image acquisition unit  120  may acquire the plurality of images of an area forward of vehicle  200  (or to the sides or rear of a vehicle, for example) at multiple locations as vehicle travels along road segment  3700 . For example, image acquisition unit  120  may obtain images using image capture device  122  having a field of view  202  at each of locations  3752 - 3768  and  3712  (see  FIGS. 37, 38 ). In other exemplary embodiments, image acquisition unit  120  may acquire images from one or more of image capture devices  122 ,  124 ,  126 , having fields of view  202 ,  204 ,  206  at each of locations  3752 - 3768  and  3712 . Image acquisition unit  120  may transmit the one or more images to processing unit  110  over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). Images obtained by the one or more image capture devices  122 ,  124 ,  126  may be stored in one or more of memories  140 ,  150 , and/or database  160 . 
     Process  3900  may also include a step  3904  of determining traveled trajectory  3720 . Processing unit  110  may receive the one or more images from image acquisition unit  120 . Processing unit  110  may execute processes similar to those discussed with respect to  FIGS. 34-36  to identify locations  3752 - 3768  of vehicle  200  in the plurality of images. For example, processing unit  110  may identify one or more landmarks and use directional vectors of the landmarks to determine locations  3752 - 3768  and current location  3712  using the systems and methods disclosed with respect to  FIGS. 34-36 . Processing unit  110  may determine traveled trajectory  3720  based on the determined locations  3752 - 3768  and current location  3712  of vehicle  200 . In one exemplary embodiment, processing unit  110  may determine traveled trajectory  3720  by curve-fitting a three-dimensional polynomial to the determined locations  3752 - 3768  and current location  3712  of vehicle  200 . 
     In some embodiments, processing unit  110  may execute monocular image analysis module  402  to perform multi-frame analysis on the plurality of images. For example, processing unit  110  may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit  110  may then use the 3D-map to detect the road surface as well as to generate traveled trajectory  3720  of vehicle  200 . 
     Process  3900  may include a step  3906  of determining a current location  3712  of vehicle  200 . Processing unit  110  may determine current location  3712  of vehicle  200  by performing processes similar to those discussed, for example, with respect to  FIGS. 34-36  regarding navigation based on recognized landmarks. In some exemplary embodiments, processing unit  110  may determine current location  3712  based on signals from position sensor  130 , for example, a GPS sensor. In another exemplary embodiment, processing unit  110  may determine current location  3712  of vehicle  200  by integrating a velocity of vehicle  200  as vehicle  200  travels along traveled trajectory  3720 . For example, processing unit  110  may determine a time “t” required for vehicle  200  to travel between two locations  3751  and  3712  on traveled trajectory  3720 . Processing unit  110  may integrate the velocity of vehicle  200  over time t to determine current location  3712  of vehicle  200  relative to location  3751 . 
     Process  3900  may also include a step  3908  of determining whether current location  3712  of vehicle  200  is located on predetermined road model trajectory  3710 . In some exemplary embodiments, predetermined road model trajectory  3710  may be represented by a three-dimensional polynomial of a target trajectory along road segment  3700 . Processing unit  110  may retrieve predetermined road model trajectory  3710  from database  160  stored in one or memories  140  and  150  included in vehicle  200 . In some embodiments, processing unit  110  may retrieve predetermined road model trajectory  3710  from database  160  stored at a remote location via a wireless communications interface. 
     Processing unit  110  may determine whether current location  3712  of vehicle  200  is located on predetermined road model trajectory  3710 , using processes similar to those discussed with respect to  FIGS. 34-37 , by for example, determining a distance between vehicle  200  and a recognized landmark. When processing unit  110  determines that current location of vehicle  200  is on predetermined road model trajectory  3710  (see  FIG. 37 ), processing unit  110  may proceed to step  3912 . When processing unit  110  determines, however, that current location of vehicle  200  is not on predetermined road model trajectory  3710  (see  FIG. 38 ), processing unit  110  may proceed to step  3910 . 
     In step  3910 , processing unit  110  may determine a lateral offset “d” that may help ensure that vehicle  200  may move from current location  3712  to target location  3714  on predetermined road model trajectory  3710 . Processing unit  110  may determine lateral offset d. In one embodiment, processing unit  110  may determine lateral offset d by determining the left and right sides  3706 ,  3708 . In one other exemplary embodiments, processing unit  110  may determine a translation function needed to convert current location  3712  to target location  3714 . In another embodiment, processing unit  110  may determine the translation function by reducing the error between current location  3712  and target location  3714 . In additional exemplary embodiments, processing unit  110  may determine the lateral offset d by observing (using one or more onboard cameras and one or more images captured by those cameras) left side  3706  and right side  3708  of road segment  3700 . After determining the lateral offset d, processing unit may proceed to step  3912 . 
     Process  3900  may include a step  3912  of determining heading direction  3730  of vehicle  200 , and possibly a correction to the current location  3712  computed in step  3906 . In one exemplary embodiment, processing unit  110  may determine heading direction  3730  and a correction to location  3712  by aligning the traveled trajectory  3720  at current location  3712  with the model trajectory  3710 . The alignment procedure may provide a rigid transformation that reduces or minimizes the distance between  3720  and  3712 . In one exemplary embodiment, processing unit  110  may compute a rigid transformation with four degrees of freedom, accounting for 3D rotation (heading) and 1D longitudinal translation. In another exemplary embodiment, processing unit  110  may compute a rigid transformation with any number of parameters (degrees of freedom) between 1 and 6. After alignment, processing unit  110  may determine the predicted location  3774  (see  FIGS. 37, 38 ) of vehicle  200  after time “t” based on a current velocity of vehicle  200  and the geometry of the model trajectory  3710 . 
     In other exemplary embodiments, in step  3912 , processing unit  110  may determine heading direction  3730  of vehicle  200 , and possibly a correction to the current location  3712  computed in step  3906 . For example, processing unit  110  may determine heading direction  3730  and improved location  3712  by aligning the traveled trajectory  3720  at current location  3712  with the model trajectory  3710 . The alignment procedure may find a rigid transformation that minimizes the distance between  3720  and  3712 . In one exemplary embodiment, processing unit  110  may compute a rigid transformation with four degrees of freedom, accounting for 3D rotation (heading) and 1D longitudinal translation. In another exemplary embodiment, processing unit  110  may compute a rigid transformation with any number of parameters (degreed of freedom) between 1 and 6. After alignment, processing unit  110  may determine the predicted location  3774  (see  FIGS. 37, 38 ) of vehicle  200  after time “t” based on a current velocity of vehicle  200  and the geometry of the model trajectory  3710 . 
     In yet other exemplary embodiments, processing unit  110  may determine heading direction  3730  and a location  3712  as a gradient of traveled trajectory  3720  at current location  3712  of vehicle  200 . For example, processing unit  110  may obtain a slope of a three-dimensional polynomial representing traveled trajectory  3720  to determine heading direction  3730  of vehicle  200 . In another exemplary embodiment, process  110  may project traveled trajectory  3720  forward from current location  3712 . In projecting traveled trajectory  3720 , processing unit  110  may determine a predicted location  3774  (see  FIGS. 37, 38 ) of vehicle  200  after time “t” based on a current velocity of vehicle  200 . 
     Processing unit  110  may also determine predicted location  3774  of vehicle  200  after time “t” based on one of many cues. For example, processing unit  110  may determine predicted location  3774  of vehicle  200  after time “t” based on a left lane mark polynomial, which may be a polynomial representing left side  3706  of road segment  3700 . Thus, for example, processing unit  110  may determine left position  3770  (see  FIGS. 37, 38 ) on the left lane mark polynomial corresponding to current location  3712  of vehicle  200 . Processing unit  110  may determine location  3770  by determining the distance “D” between current location  3712  and left side  3706  based on the left lane mark polynomial. It is contemplated that when vehicle  200  is not located on predetermined road model trajectory  3710  (as in  FIG. 38 ), processing unit  110  may determine distance D as the distance between target location  3714  and left side  3706 . Processing unit  110  may also determine a location  3772  on left side  3706  after time “t” using the mathematical representation of the left lane mark polynomial and current velocity of vehicle  200 . Processing unit  110  may determine predicted location  3774  of vehicle  200  by laterally offsetting the determined location  3773  on left side  3706  by distance D. In another exemplary embodiment, processing unit  110  may determine the location of vehicle  200  after time “t” based on a right lane mark polynomial, which may be a polynomial representing right side  3708  of road segment  3700 . Processing unit  110  may perform processes similar to those discussed above with respect to left lane mark polynomial to determine predicted position  3774  of vehicle  200  based on right lane mark polynomial. 
     In some exemplary embodiments, processor  110  may determine the location of vehicle  200  after time “t” based on the trajectory followed by a forward vehicle, which may be traveling in front of vehicle  200 . In other exemplary embodiments, processing unit  200  may determine the location of vehicle  200  after time “t” by determining an amount of free space ahead of vehicle  200  and a current velocity of vehicle  200 . In some embodiments, processing unit  200  may determine the location of vehicle  200  after time “t” based on virtual lanes or virtual lane constraints. For example, when processing unit  110  detects two vehicles traveling in front of vehicle  200 , one in each adjacent lane, processing unit  110  may use the average lateral distance between the two vehicles in front as a trajectory (virtual lane marker), which may be used to determine a position of vehicle  200  after time “t.” In other embodiments, processing unit  110  may use mathematical representations of left side  3706  (i.e. left lane mark polynomial) and right side  3708  (i.e. right lane mark polynomial) as defining virtual lane constraints. Processing unit  110  may determine predicted position  3774  of vehicle  200  based on the virtual lane constraints (i.e. based on both the left and the right lane mark polynomials) and an estimated location of vehicle  200  from the left and right sides  3706 ,  3708 . 
     In other embodiments, processing unit  110  may determine predicted location  3774  of vehicle  200  after time “t” based on following a trajectory predicted using holistic path prediction methods. In some exemplary embodiments, processing unit  110  may determine predicted location  3774  of vehicle  200  after time “t” by applying weights to some or all of the above-described cues. For example, processing unit  110  may determine the location of vehicle  200  after time “t” as a weighted combination of the locations predicted based on one or more of a left lane mark polynomial model, a right lane mark polynomial model, holistic path prediction, motion of a forward vehicle, determined free space ahead of the autonomous vehicle, and virtual lanes. Processing unit  110  may use current location  3712  of vehicle  200  and predicted location  3774  after time “t” to determine heading direction  3730  for vehicle  200 . 
     In some embodiments, in step  3912  of process  3900 , processing unit  110  may also estimate a longitudinal offset. For example, processing unit  110  may solve for the heading and the offset by an alignment procedure, between the model trajectory and the tail of vehicle  200 . 
     Process  3900  may also include a step  3914  of determining steering direction  3740 . In one exemplary embodiment, processing unit  110  may obtain a mathematical representation (e.g. three-dimensional polynomial) of predetermined road model trajectory  3710 . Processing unit  110  may determine steering direction  3740  as a vector oriented tangentially to predetermined road model trajectory  3710  at target location  3714 . For example, processing unit  110  may determine direction  3740  as a vector pointing along a gradient of the mathematical representation of predetermined road model trajectory  3710  at target location  3714 . 
     Process  3900  may also include a step  3916  of adjusting steering system  240  of vehicle  200  based on the transformation determined, for example, in steps  3910 - 3914 . The required transformation may include lateral offset d. The transformation may further include rotation by an angle to help ensure that heading direction  3730  of vehicle  200  may be aligned with steering direction  3740 . Although,  FIGS. 37, 38  illustrate determination of one angle between heading direction  3730  and steering direction  3740 , it is contemplated that in three-dimensional space, rotation along three angles in three generally orthogonal planes may be required to ensure that heading direction  3730  may be aligned with steering direction  3730 . One of ordinary skill in the art would, therefore, recognize that the transformation determined in steps  3910 - 3914  may include at least three rotational angles and at least one translation (i.e. lateral offset). 
     Processing unit  110  may send control signals to steering system  240  to adjust rotation of the wheels of vehicle  200  so that heading direction  3730  may be aligned with steering direction  3740  and vehicle  200  may move from current location  3712  to target location  3714  when vehicle  200  is located off predetermined road model trajectory  3710 . Processing unit  110  and/or image acquisition unit  120  may repeat steps  3902  through  3916  after a predetermined amount of time. In one exemplary embodiment, the predetermined amount of time may range between about 0.5 seconds to 1.5 seconds. By repeatedly determining lateral offset d and rotation angles, processing unit  110  and/or image acquisition unit  120  may help to navigate vehicle  200 , using tail alignment, along road segment  3700 . 
     As discussed in other sections, navigation of an autonomous vehicle along a road segment may include the use of one or more recognized landmarks. Among other things, such recognized landmarks may enable the autonomous vehicle to determine its current location with respect to a target trajectory from sparse data model  800 . The current location determination using one or more recognized landmarks may be more precise than determining a position using GPS sensing, for example. 
     Between recognized landmarks, the autonomous vehicle may navigate using a dead-reckoning technique. This technique may involve periodically estimating a current location of the vehicle with respect to the target trajectory based on sensed ego-motion of the vehicle. Such sensed ego motion may enable the vehicle (e.g., using processing unit  110 ) to not only estimate the current location of the vehicle relative to the target trajectory, but it may also enable the processing unit  110  to reconstruct the vehicle&#39;s traveled trajectory. Sensors that may be used to determine the ego motion of the vehicle may include various sensors such as, for example, onboard cameras, speedometers, and/or accelerometers. Using such sensors, processing unit  110  may sense where the vehicle has been and reconstruct the traveled trajectory. This reconstructed traveled trajectory may then be compared to the target trajectory using the tail alignment technique described above to determine what navigational changes, if any, are required to align the traveled trajectory at a current location with the target trajectory at the current location. 
     Navigating Road Junctions 
     Consistent with disclosed embodiments, the system may navigate through road junctions, which may constitute areas with few or no lane markings. Junction navigation may include 3D localization based on two or more landmarks. Thus, for example, the system may rely on two or more landmarks to determine a current location and a heading of an autonomous vehicle. Further, the system may determine a steering action based on the determined heading and a direction of a predetermine road model trajectory representing a preferred path for the vehicle. 
       FIG. 40  illustrates vehicle  200  (which may be an autonomous vehicle) traveling through road junction  4000  in which the disclosed systems and methods for navigating road junctions may be used. As illustrated in  FIG. 40 , vehicle  200  may be traveling along road segment  4002 , which may intersect with road segment  4004 . Although road segments  4002  and  4004  appear to intersect at right angles in  FIG. 40 , it is contemplated that road segments  4002  and  4004  may intersect at any angle. Further, although road segments  4002  and  4004  each have two lanes in  FIG. 40 , it is contemplated that road segments  4002  and  4004  may have any number of lanes. It is also contemplated that road segments  4002  and  4004  may have the same number or different number of lanes. 
     Vehicle  200  may travel along lane  4006  of road segment  4002 . Vehicle  200  may be equipped with three image capture devices  122 ,  124 ,  126 . Although,  FIG. 40  depicts vehicle  200  as equipped with image capture devices  122 ,  124 ,  126 , more or fewer image capture devices may be employed on any particular vehicle  200 . As illustrated in  FIG. 40 , lane  4006  of road segment  4002  may be delimited by left side  4008  and right side  4010 . A predetermined road model trajectory  4012  may define a preferred path (i.e., a target road model trajectory) within lane  4006  of road segments  4002 ,  4004  that vehicle  200  may follow as vehicle  200  travels along road segments  4002 ,  4004  through junction  4000 . In some exemplary embodiments, predetermined road model trajectory  4012  may be located equidistant from left side  4008  and right side  4010 . It is contemplated however that predetermined road model trajectory  4012  may be located nearer to one or the other of left side  4008  and right side  4010  of road segment  4002 . 
     In one exemplary embodiment, predetermined road model trajectory  4012  may be mathematically defined using a three-dimensional polynomial function. In some exemplary embodiments, processing unit  110  of vehicle  200  may be configured to retrieve predetermined road model trajectory  4012  from a database (e.g.  160 ) stored in one or more of memories  140 ,  150  included in vehicle  200 . In other exemplary embodiments, processing unit  110  of vehicle  200  may be configured to retrieve predetermined road model trajectory  4012  from a database (e.g.  160 ), which may be stored remotely from vehicle  200 , over a wireless communications interface. As illustrated in the exemplary embodiment of  FIG. 40 , predetermined road model trajectory  4012  may allow vehicle  200  to turn left from lane  4006  of road segment  4002  into lane  4014  of road segment  4004 . 
     Image acquisition unit  120  may be configured to acquire an image representative of an environment of vehicle  200 . For example, image acquisition unit  120  may obtain an image showing a view in front of vehicle  200  using one or more of image capture devices  122 ,  124 ,  126 . Processing unit  110  of vehicle  200  may be configured to detect two or more landmarks  4016 ,  4018  in the one or more images acquired by image acquisition unit  120 . Such detection may occur using the landmark detection techniques previously discussed, for example. Processing unit  110  may detect the two or more landmarks  4016 ,  4018  using one or more processes of landmark identification discussed above with reference to  FIGS. 22-28 . Although  FIG. 40  illustrates two landmarks  4016 ,  4018 , it is contemplated that vehicle  200  may detect more than two landmarks  4016 ,  4018  (i.e., three or more landmarks) based on the images acquired by image acquisition unit  120 . For example,  FIG. 40  illustrates additional landmarks  4020  and  4022 , which may be detected and used by processing unit  110 . 
     Processing unit  110  may be configured to determine positions  4024 ,  4026  of landmarks  4016 ,  4018 , respectively, relative to vehicle  200 . Processing unit  110  may also be configured to determine one or more directional indicators  4030 ,  4032  of landmarks  4016 ,  4018  relative to vehicle  200 . Further, processing unit  110  may be configured to determine current location  4028  of vehicle  200  based on an intersection of directional indicators  4030 ,  4032 . In one exemplary embodiment as illustrated in  FIG. 40 , processing unit  110  may be configured to determine current location  4028  as the intersection point of directional indicators  4030 ,  4032 . 
     Processing unit  110  may be configured to determine previous location  4034  of vehicle  200 . In one exemplary embodiment, processing unit  110  may repeatedly determine a location of vehicle  200  as vehicle  200  travels on road segments  4002  and  4004 . Thus, for example, before vehicle  200  reaches its current location  4028 , vehicle may be located at previous location  4034  and may travel from previous location  4034  to current location  4028 . Before reaching current location  4028 , processing unit  110  of vehicle  200  may be configured to determine positions  4024 ,  4026  of landmarks  4016 ,  4018 , respectively, relative to vehicle  200 . Processing unit  110  may also be configured to determine directional indicators  4036 ,  4038  of landmarks  4016 ,  4018  relative to vehicle  200 . Processing unit  110  may also be configured to determine previous location  4034  of vehicle  200  based on an intersection of directional indicators  4036 ,  4038 . In one exemplary embodiment as illustrated in  FIG. 40 , processing unit  110  may be configured to determine previous location  4034  as the intersection point of directional indicators  4036 ,  4038 . 
     Processing unit  110  may be configured to determine a direction  4040  of predetermined road model trajectory  4012  at current location  4028  of vehicle  200 . Processing unit  110  may determine direction  4040  as a direction tangential to predetermined road model trajectory  4012 . In one exemplary embodiment, processing unit  110  may be configured to determine direction  4040  based on a gradient or slope of a three-dimensional polynomial representing predetermined road model trajectory  4012 . 
     Processing unit  110  may also be configured to determine heading direction  4050  of vehicle  200 . Processing unit  110  may determine heading direction  4050  based on landmarks  4016  and  4018 . Processing unit  110  may determine heading direction  4050  based on current location  4028  and previous location  4034  of vehicle  200 . For example, processing unit  110  may determine heading direction  4050  as a vector extending from previous location  4034  towards current location  4028 . In some exemplary embodiments, processing unit  110  may determine heading direction  4050  as a direction along which image capture device  122  may be oriented relative to a local coordinate system associated with vehicle  200 . 
     Processing unit  110  may be configured to determine whether heading direction  4050  of vehicle  200  is aligned with (i.e., generally parallel to) direction  4040  of predetermined road model trajectory  4012 . When heading direction  4050  is not aligned with direction  4040  of predetermined road model trajectory  4012  at current location  4028  of vehicle  200 , processing unit  110  may determine a steering angle between heading direction  4050  of vehicle  200  and direction  4040  of predetermined road model trajectory  4012 . In one exemplary embodiment, processing unit  110  may also determine, for example, a reduction or acceleration in a current velocity of vehicle  200  required to help ensure that heading direction  4050  of vehicle  200  may be aligned with direction  4040  of predetermined road model trajectory  4012  in a predetermined amount of time. Processing unit  110  may be configured to execute instructions stored in navigational response module  408 , for example, to transmit a control signal specifying the steering angle to steering system  240  of the vehicle. Steering system  200 , in turn, may be configured to rotate wheels of vehicle  200  to help ensure that heading direction  4050  of vehicle  200  may be aligned with direction  4040  of predetermined road model trajectory  4012 . 
     Image acquisition unit  120  may repeatedly acquire an image of the environment in front of vehicle  200 , for example, after a predetermined amount of time. Processing unit  110  may also be configured to repeatedly detect landmarks  4016 ,  4018 ,  4020 ,  4022 , etc., in the image acquired by image acquisition unit  120  and determine the steering angle as discussed above. Thus, image acquisition unit  120  and processing unit  110  may cooperate to navigate vehicle  200  through junction  400  using two or more of landmarks  4016 ,  4018 ,  4020 ,  4022 . 
       FIG. 41  is a flowchart showing an exemplary process  4100 , for navigating vehicle  200  through junction  4000 , using two or more landmarks  4016 ,  4018 ,  4020 ,  4022 , consistent with disclosed embodiments. Steps of process  4100  may be performed by one or more of processing unit  110  and image acquisition unit  120 , with or without the need to access memory  140  or  150 . The order and arrangement of steps in process  4100  is provided for purposes of illustration. As will be appreciated from this disclosure, modifications may be made to process  4100  by, for example, adding, combining, removing, and/or rearranging the steps for the process. 
     As illustrated in  FIG. 41 , process  4100  may include a step  4102  of acquiring an image representative of an environment of the vehicle. In one exemplary embodiment, image acquisition unit  120  may acquire one or more images of an area forward of vehicle  200  (or to the sides or rear of a vehicle, for example). For example, image acquisition unit  120  may obtain an image using image capture device  122  having a field of view  202 . In other exemplary embodiments, image acquisition unit  120  may acquire images from one or more of image capture devices  122 ,  124 ,  126 , having fields of view  202 ,  204 ,  206 . Image acquisition unit  120  may transmit the one or more images to processing unit  110  over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). 
     Process  4100  may also include a step  4104  of identifying two or more landmarks  4016 ,  4018 ,  4020 ,  4022  in the one or more images. Processing unit  110  may receive the one or more images from image acquisition unit  120 . Processing unit  110  may execute monocular image analysis module  402  to analyze the plurality of images at step  4104 , as described in further detail in connection with  FIGS. 5B-5D . By performing the analysis, processing unit  110  may detect a set of features within the set of images, for example, two or more landmarks  4016 ,  4018 ,  4020 ,  4022 . Landmarks  4016 ,  4018 ,  4020 ,  4022  may include one or more traffic signs, arrow markings, lane markings, dashed lane markings, traffic lights, stop lines, directional signs, reflectors, landmark beacons, lampposts, a change in spacing of lines on the road, signs for businesses, and the like. 
     In some embodiments, processing unit  110  may execute monocular image analysis module  402  to perform multi-frame analysis on the plurality of images to detect two or more landmarks  4016 ,  4018 ,  4020 ,  4022 . For example, processing unit  110  may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit  110  may then use the 3D-map to detect the road surface, as well as landmarks  4016 ,  4018 ,  4020 ,  4022 . In another exemplary embodiment, image processor  190  of processing unit  110  may combine a plurality of images received from image acquisition unit  120  into one or more composite images. Processing unit  110  may use the composite images to detect the two or more landmarks  4016 ,  4018 ,  4020 ,  4022 . For example, in some embodiments, processing unit  110  may perform stereo processing of images from two or more image capture devices. 
     Process  4100  may also include a step  4106  of determining directional indicators  4030 ,  4032  associated with at least two landmarks  4016 ,  4018 , respectively. Processing unit  110  may determine directional indicators  4030 ,  4032  based on the positions  4024 ,  4026  of the at least two landmarks  4016 ,  4018 , respectively, relative to vehicle  200 . For example, processing unit  110  may receive landmark positions  4024 ,  4026  for landmarks  4016 ,  4018 , respectively, from information, which may be stored in one or more databases in memory  140  or  150 . Processing unit  110  may determine directional indicator  4030  as a vector extending from vehicle  200  towards landmark position  4024 . Likewise, processing unit  110  may determine directional indicator  4032  as a vector extending from vehicle  200  towards landmark position  4026 . Although two landmarks  4016 ,  4018  are referenced in the above discussion, it is contemplated that processing unit  110  may determine landmark positions  4024 ,  4026 , and directional indicators  4030 ,  4032  for more than two landmarks  4016 ,  4018  (e.g., for landmarks  4020 ,  4022 ). 
     Process  4100  may include a step  4108  of determining current location  4028  of vehicle  200 . Processing unit  110  may determine current location  4028  based on an intersection of directional indicators  4030  and  4032  of landmarks  4016 ,  4018 , respectively (e.g., at an intersection point of directional indicators  4030  and  4032 ). Process  4100  may include a step  4110  of determining previous location  4034  of vehicle  200 . As discussed above, processing unit  110  may be configured to determine previous location  4034  of vehicle  200  based on two or more landmarks  4016 ,  4018 ,  4020 ,  4022 . In one exemplary embodiment, processing unit  110  may repeatedly determine a location of vehicle  200  using two or more landmarks  4016 ,  4018 ,  4020 ,  4022  as vehicle  200  moves on road segments  4002  and  4004 . Thus, for example, before vehicle  200  reaches its current location  4028 , vehicle may be located at previous location  4034  and may travel from previous location  4034  to current location  4028 . Before reaching current location  4028 , processing unit  110  of vehicle  200  may be configured to determine positions  4024 ,  4026  of landmarks  4016 ,  4018 , respectively, relative to vehicle  200 . Processing unit  110  may perform processes similar to those discussed above with respect to step  4108  to determine previous location  4034  of vehicle  200 . For example, processing unit  110  may be configured to determine directional indicators  4036 ,  4038  of landmarks  4016 ,  4018  relative to vehicle  200 . Processing unit  110  may also be configured to determine previous location  4034  of vehicle  200  based on an intersection of directional indicators  4036 ,  4038  (e.g. at an intersection point of directional indicators  4036  and  4038 ). 
     Process  4100  may include a step  4112  of determining heading direction  4050  of vehicle  200 . As discussed above, processing unit  110  may determine heading direction  4050  based on current location  4028  and previous location  4034  of vehicle  200 , both of which may be determined using two or more of landmarks  4016 ,  4018 ,  4020 ,  4022 . In one exemplary embodiment, processing unit  110  may determine heading direction  4050  as a vector extending from previous location  4034  towards current location  4028 . In another exemplary embodiment, processing unit  110  may determine heading direction  4050  as a direction along which image capture device  122  may be oriented relative to a local coordinate system associated with vehicle  200 . Although only two landmarks  4016 ,  4018  have been described with respect to determining current location  4028  and previous location  4024  of vehicle  200 , it is contemplated that processing unit may use more than two landmarks  4016 ,  4018  to determine current location  4028  and previous location  4024  of vehicle  200  and heading direction  4050 . 
     Process  4100  may include a step  4114  of determining direction  4040  of predetermined road model trajectory  4012  at current location  4028  of vehicle  200 . In one exemplary embodiment, processing unit  110  may obtain a mathematical representation (e.g. three-dimensional polynomial) of predetermined road model trajectory  4012 . Processing unit  110  may determine direction  4040  as a vector oriented tangentially to predetermined road model trajectory  4012  at current location  4028  of vehicle  200 . For example, processing unit  110  may determine direction  4040  as a vector pointing along a gradient of the mathematical representation of predetermined road model trajectory  4012  at current location  4028  of vehicle  200 . Although the above description assumes that current location  4028  and previous location  4034  of vehicle  200  are located on predetermined road model trajectory  4012 , processing unit  110  may perform processes similar to those discussed above with respect to  FIGS. 34-39  when vehicle  200  is not located on predetermined road model trajectory  4012 . For example, processing unit  110  may determine a transform required to move vehicle  200  to predetermined road model trajectory  4012  before determining direction  4040  as discussed above. 
     Process  4100  may also include a step  4116  of determining steering angle θ for vehicle  200 . Processing unit  110  may also determine steering angle θ as an angle between heading direction  4050  and direction  4040  of predetermined road model trajectory  4012  at current location  4028  of vehicle  200 . Processing unit  110  may execute instructions in navigational module  408 , for example, to transmit a control signal specifying steering angle θ to steering system  240 . Steering system  240  may help adjust, for example, a steering wheel of vehicle  200  to turn the wheels of vehicle  200  to help ensure that heading direction  4050  of vehicle  200  may be aligned (i.e., parallel) with direction  4040  of predetermined road model trajectory  4012 . 
     Processing unit  110  and/or image acquisition unit  120  may repeat steps  4102  through  4116  after a predetermined amount of time. In one exemplary embodiment, the predetermined amount of time may range between about 0.5 seconds to 1.5 seconds. By repeatedly determining current location  4028 , heading direction  4050 , direction  4040  of predetermined road model trajectory  4012  at current location  4028 , and steering angle θ required to align heading direction  4050  with direction  4040 , processing unit  110  may transmit one or more control signals to one or more of throttling system  220 , steering system  240 , and braking system  230  to navigate vehicle  200  through road junction  4000 , using two or more landmarks  4016 ,  4018 ,  4020 ,  4022 . 
     Navigation Using Local Overlapping Maps 
     Consistent with disclosed embodiments, the system may use a plurality of local maps for navigation. Each map may have its own arbitrary coordinate frame. To ease the transition in navigating from one local map to another, the maps may include an overlap segment, and navigation in the overlap segment may be based on both of the overlapping maps. 
       FIG. 43  illustrates first and second local maps  4200  and  4202  associated with first and second road segments  4204  and  4206 , respectively. First road segment  4204  may be different from second road segment  4206 . Maps  4200  and  4202  may each have their own arbitrary coordinate frame. Maps  4200  and  4202  may also each constitute a sparse map having the same or different data densities. In one exemplary embodiment, maps  4200  and  4202  may each have a data density of no more than 10 kilobytes per kilometer. Of course, local maps  4200  and  4202  may include other data density values, such as any of the data densities previously discussed relative to sparse map  800 , for example. Vehicle  200  (which may be an autonomous vehicle) traveling on a first road segment  4204  and/or on second road segment  4206  may use the disclosed systems and methods for navigation. Vehicle  200  may include at least one image capture device  122 , which may be configured to obtain one or more images representative of an environment of the autonomous vehicle. Although  FIG. 42  depicts vehicle  200  as equipped with image capture devices  122 ,  124 ,  126 , more or fewer image capture devices may be employed on any particular vehicle  200 . As illustrated in  FIG. 42 , map  4200  may include road segment  4204 , which may be delimited by left side  4208  and right side  4210 . A predetermined road model trajectory  4212  may define a preferred path (i.e., a target road model trajectory) within road segment  4204 . Predetermined road model trajectory  4212  may be mathematically represented by a three-dimensional polynomial. Vehicle  200  may follow predetermined road model trajectory  4212  as vehicle  200  travels along road segment  4204 . In some exemplary embodiments, predetermined road model trajectory  4212  may be located equidistant from left side  4208  and right side  4210 . It is contemplated however that predetermined road model trajectory  4212  may be located nearer to one or the other of left side  4208  and right side  4210  of road segment  4204 . As also illustrated in  FIG. 42 , a portion of road segment  4204  between delimiting points A and B may represent an overlap segment  4220 . As will be described later, overlap segment  4220  between positions A and B of road segment  4204  may overlap with a portion of road segment  4206 . Further, although  FIG. 42  illustrates one lane in road segment  4204 , it is contemplated that road segment  4204  may have any number of lanes. It is also contemplated that vehicle  200  traveling along any lane of road segment  4204  may be navigated according to the disclosed methods and systems. Further, in some embodiments, a road segment may extend between two known locations such as, for example, two intersections. 
     As also illustrated in  FIG. 42 , map  4202  may include road segment  4206 , which may be delimited by left side  4222  and right side  4224 . A predetermined road model trajectory  4226  may define a preferred path (i.e., a target road model trajectory) within road segment  4206 . Predetermined road model trajectory  4226  may be mathematically represented by a three-dimensional polynomial. Vehicle  200  may follow predetermined road model trajectory  4226  as vehicle  200  travels along road segment  4206 . In some exemplary embodiments, predetermined road model trajectory  4226  may be located equidistant from left side  4222  and right side  4224 . It is contemplated however that predetermined road model trajectory  4226  may be located nearer to one or the other of left side  4222  and right side  4224  of road segment  4206 . As also illustrated in  FIG. 42 , a portion of road segment  4206  between delimiting points A′ and B′ may represent overlap segment  4220 , which may overlap with overlap segment  4220  between delimiting points A and B of road segment  4204 . Although  FIG. 42  illustrates one lane in road segment  4206 , it is contemplated that road segment  4206  may have any number of lanes. It is also contemplated that vehicle  200  traveling along any lane of road segment  4206  may be navigated according to the disclosed methods and systems. 
     As used in this disclosure, the term overlap indicates that overlap segment  4220  represents the same portion of the road that may be traveled on by vehicle  200 . In some embodiments, an overlap segment  4220  may include a segment of map  4200  that represents a road segment and associated road features (such as landmarks, etc.) that are also represented by a corresponding segment (i.e., the overlap segment) of map  4222 . As a result, overlap segment  4220  may include portions of road segments  4204 ,  4206  having the same size (length, width, height, etc.), shapes (orientation and inclination, etc.), etc. Moreover, the shapes and lengths of predetermined road model trajectories  4212  and  4226  in the overlap segment  4220  may be similar. However, because maps  4200  and  4202  may have different local coordinate systems, the mathematical representations (e.g., three-dimensional polynomials) of predetermined road model trajectories  4212  and  4226  may differ in the overlap segment  4220 . In one exemplary embodiment, overlap segment  4220  may have a length ranging between 50 m and 150 m. 
     Image acquisition unit  120  may be configured to acquire an image representative of an environment of vehicle  200 . For example, image acquisition unit  120  may obtain an image showing a view in front of vehicle  200  using one or more of image capture devices  122 ,  124 ,  126 . Processing unit  110  of vehicle  200  may be configured to detect a current location  4214  of vehicle  200  using one or more navigational processes discussed above with reference to  FIGS. 34-36 . Processing unit  110  may also be configured to determine whether current position  4214  of vehicle  200  lies on road segment  4204  or  4206  using one or more processes of determining intersection points of directional vectors for recognized landmarks with one or more of predetermined road model trajectories  4212  and  4226  as discussed above with reference to  FIGS. 34-36 . Furthermore, processing unit  110  may be configured to determine whether current location  4214  of vehicle  200  lies on road segment  4204 , road segment  4206 , or in the overlap segment  4220 , using similar processes discussed above with reference to  FIGS. 34-36 . 
     When vehicle  200  is located on road segment  4204 , processing unit  110  may be configured to align a local coordinate system of vehicle  200  with a local coordinate system associated with road segment  4204 . After aligning the two coordinate systems, processing unit  110  may be configured to determine a direction  4230  of predetermined road model trajectory  4212  at current location  4214  of vehicle  200 . Processing unit  110  may determine direction  4230  as a direction tangential to predetermined road model trajectory  4212 . In one exemplary embodiment, processing unit  110  may be configured to determine direction  4230  based on a gradient or slope of a three-dimensional polynomial representing predetermined road model trajectory  4212 . 
     Processing unit  110  may also be configured to determine heading direction  4240  of vehicle  200 . As illustrated in  FIG. 42 , heading direction  4240  of vehicle  200  may be a direction along which image capture device  122  may be oriented relative to the local coordinate system associated with vehicle  200 . Processing unit  110  may be configured to determine whether heading direction  4240  of vehicle  200  is aligned with (i.e., generally parallel to) direction  4230  of predetermined road model trajectory  4212 . When heading direction  4240  is not aligned with direction  4230  of predetermined road model trajectory  4212  at current location  4214  of vehicle  200 , processing unit  110  may determine a first autonomous navigational response (ANR) that may help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230  of predetermined road model trajectory  4212 . 
     In one exemplary embodiment, first ANR may include, for example, a determination of an angle by which the steering wheel or front wheels of vehicle  200  may be turned to help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230  of predetermined road model trajectory  4212 . In another exemplary embodiment, first autonomous navigational response may also include a reduction or acceleration in a current velocity of vehicle  200  to help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230  of predetermined road model trajectory  4212  in a predetermined amount of time. Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger first ANR by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of an angle  1 . Rotation by angle  1  may help align heading direction  4240  of vehicle  200  with direction  4230 . 
     When vehicle  200  is located on road segment  4206 , processing unit  110  may be configured to align a local coordinate system of vehicle  200  with a local coordinate system associated with road segment  4206 . After aligning the two coordinate systems, processing unit  110  may be configured to determine a direction  4250  of predetermined road model trajectory  4226  at current location  4214  of vehicle  200 . Processing unit  110  may determine direction  4250  as a direction tangential to predetermined road model trajectory  4226 . In one exemplary embodiment, processing unit  110  may be configured to determine direction  4250  based on a gradient or slope of a three-dimensional polynomial representing predetermined road model trajectory  4226 . 
     Processing unit  110  may also be configured to determine heading direction  4260  of vehicle  200 . As illustrated in  FIG. 42 , heading direction  4260  of vehicle  200  may be a direction along which image capture device  122  may be oriented relative to the local coordinate system associated with vehicle  200 . Processing unit  110  may be configured to determine whether heading direction  4260  of vehicle  200  is aligned with (i.e., generally parallel to) direction  4250  of predetermined road model trajectory  4226 . When heading direction  4260  is not aligned with direction  4250  of predetermined road model trajectory  4226  at current location  4214  of vehicle  200 , processing unit  110  may determine a second ANR that may help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250  of predetermined road model trajectory  4226 . 
     In one exemplary embodiment, the second ANR may include, for example, a determination of an angle  2  by which the steering wheel or front wheels of vehicle  200  may be turned to help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250  of predetermined road model trajectory  4226 . In another exemplary embodiment, the second ANR may also include a reduction or acceleration in a current velocity of vehicle  200  to help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250  of predetermined road model trajectory  4226  in a predetermined amount of time. Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger second ANR by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of angle  2 . Rotation by angle  2  may help align heading direction  4260  of vehicle  200  with direction  4250 . 
     When vehicle  200  is located on overlap segment  4220  of road segments  4204 ,  4206 , processing unit  110  may be configured to align the local coordinate system of vehicle  200  with both the local coordinate system associated with road segment  4204  as well as the local coordinate system associated with road segment  4206 . Thus, processing unit  110  may be configured to determine a third ANR based on both maps  4200  and  4202 . In one exemplary embodiment, processing unit  110  may determine the third ANR as an angle  3  by which the steering wheel or front wheels of vehicle  200  may be turned to help ensure that heading direction  4240  of vehicle  200  may be aligned with heading direction  4230  of predetermined road model trajectory, and heading direction  4260  of vehicle  200  may be aligned with direction  4250  of predetermined road model trajectory  4226 . Thus, for example, processing unit  110  may determine angle θ 3  as a combination of angles  1  and  2 . 
     Image acquisition unit  120  may repeatedly acquire an image of the environment in front of vehicle  200 , for example, after a predetermined amount of time. Processing unit  110  may also be configured to repeatedly detect whether current location  4214  of vehicle  200  lies on road segment  4204 , road segment  4206 , or in overlap segment  4220 . Processing unit  110  may determine first, second, or third ANR (e.g., angles  1 ,  2 , or  3 ) based on where vehicle  200  is located on road segments  4204 ,  4206 . Thus, image acquisition unit  120  and processing unit  110  may cooperate to navigate vehicle  200  along road segments  4204  and  4206  using overlap segment  4220 . 
       FIGS. 43A-C  include flowcharts showing an exemplary process  4300 , for navigating vehicle  200  along road segments  4204 ,  4206 , using overlapping maps  4200 ,  4202 , consistent with disclosed embodiments. Steps of process  4300  may be performed by one or more of processing unit  110  and image acquisition unit  120 , with or without the need to access memory  140  or  150 . The order and arrangement of steps in process  4300  is provided for purposes of illustration. As will be appreciated from this disclosure, modifications may be made to process  4300  by, for example, adding, combining, removing, and/or rearranging the steps of process  4300 . 
     As illustrated in  FIG. 43A , process  4300  may include a step  4302  of acquiring an image representative of an environment of the vehicle. In one exemplary embodiment, image acquisition unit  120  may acquire one or more images of an area forward of vehicle  200  (or to the sides or rear of a vehicle, for example). For example, image acquisition unit  120  may obtain an image using image capture device  122  having a field of view  202 . In other exemplary embodiments, image acquisition unit  120  may acquire images from one or more of image capture devices  122 ,  124 ,  126 , having fields of view  202 ,  204 ,  206 . Image acquisition unit  120  may transmit the one or more images to processing unit  110  over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). 
     Process  4300  may also include a step  4302  of determining current location  4214  of vehicle  200 . Processing unit  110  may receive the one or more images from image acquisition unit  120 . Processing unit  110  may execute monocular image analysis module  402  to analyze the plurality of images at step  4302 , as described in further detail in connection with  FIGS. 5B-5D . By performing the analysis, processing unit  110  may detect a set of features within the set of images, for example, one or more landmarks. Processing unit  110  may use the landmarks and perform processes similar to those discussed above, for example, in  FIGS. 34-36  to determine current location  4214  of vehicle  200 . 
     Process  4300  may include a step  4306  of determining whether vehicle  200  is located on first road segment  4304 . Processing unit  110  may determine whether vehicle  200  is located on first road segment  4304  in many ways. For example, processing unit may compare its current location  4214  determined in, for example, step  4304  with predetermined road model trajectory  4212  to determine whether current position  4214  is located on predetermined road model trajectory  4212 . Processing unit may determine that vehicle  200  is located on first road segment  4304  when current position  4214  is located on predetermined road model trajectory  4212 . In another exemplary embodiment, processing unit  110  may use landmarks and directional indicators for the landmarks to determine whether a current position  4214  of vehicle  200  is located on road segment  4204 . For example, as discussed above with respect to  FIGS. 34-36 , if a directional indicator of a recognized landmark intersects with predetermined road model trajectory  4212  (discussed above, e.g., in relation to  FIGS. 34-36 ), processing unit  110  may determine that current location  4214  of vehicle  200  lies in road segment  4204 . When processing unit  110  determines that vehicle  200  is located in road segment  4204  (Step  4306 : Yes), processing unit  110  may proceed to step  4308 . When processing unit  110  determines, however, that vehicle  200  is not located on road segment  4204  (Step  4306 : No), processing unit  110  may proceed to step  4314  via process segment C. 
     In step  4308 , processing unit  110  may determine whether vehicle  200  is located in overlap segment  4220 . Processing unit  110  may use processes similar to those discussed above with respect to step  4306  to determine whether vehicle  200  is located in overlap segment  4220 . For example, processing unit  110  may determine whether a direction indicator corresponding to a recognized landmark intersects predetermined road model trajectory  4212  in the portion of predetermined road model trajectory  4212  located between A and B in overlap segment  4220 . In another exemplary embodiment, processing unit  110  may compare current location  4214  of vehicle  200  with the mathematical representation of predetermined road model trajectory  4212  to determine whether vehicle  200  is located in overlap segment  4220 . In yet another exemplary embodiment, processing unit  110  may determine a distance traveled by vehicle  200  along predetermined road model trajectory  4212  in first road segment  4204 . Processing unit may determine the distance traveled using processes similar to those discussed above with respect to  FIGS. 37-39  regarding navigation using tail alignment. Processing unit  110  may determine whether current location  4214  of vehicle  200  lies in overlap segment  4220  based on the distance traveled by vehicle  200 . When processing unit  110  determines that vehicle  200  is located within overlap segment  4220  (Step  4308 : Yes), processing unit  110  may proceed to step  4320  via process segment D. When processing unit  110  determines, however, that vehicle  200  is not located within overlap segment  4220  (Step  4308 : No), processing unit  110  may proceed to step  4310 . 
     Process  4300  may include a step  4310  of determining first ANR. Processing unit  110  may determine first ANR based on its determination that vehicle  200  is located in first road segment  4204  but not in overlap segment  4220 . In one exemplary embodiment, processing unit  110  may obtain a mathematical representation (e.g. three-dimensional polynomial) of predetermined road model trajectory  4212 . Processing unit  110  may determine direction  4230  of predetermined road model trajectory  4212  as a vector oriented tangentially to predetermined road model trajectory  4212  at current location  4214  of vehicle  200 . For example, processing unit  110  may determine direction  4230  as a vector pointing along a gradient of the mathematical representation of predetermined road model trajectory  4212  at current location  4214 . Although the above description assumes that current location  4214  of vehicle  200  is located on predetermined road model trajectory  4212 , processing unit  110  may perform processes similar to those discussed above with respect to  FIGS. 34-39  when vehicle  200  is not located on predetermined road model trajectory  4212 . For example, processing unit may determine a transform required to move vehicle  200  to predetermined road model trajectory  4212  before determining direction  4230  as discussed above. 
     Processing unit  110  may also determine a heading direction  4240  of vehicle  200 . For example, as illustrated in  FIG. 42 , processing unit  110  may determine heading direction  4240  of vehicle  200  as the direction in which image capture device  122  may be oriented relative to a local coordinate system associated with vehicle  200 . In another exemplary embodiment, processing unit  200  may determine heading direction  4240  as the direction of motion of vehicle  200  at current location  4214 . In yet another exemplary embodiment, processing unit may determine heading direction  4240  based on a traveled trajectory as discussed above with respect to  FIGS. 37-39 . Processing unit  110  may determine a rotational angle θ 1  between heading direction  4240  and direction  4230  of predetermined road model trajectory  4212 . In one exemplary embodiment, first ANR may include rotation angle  1  that may help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230  of predetermined road model trajectory  4212 . In another exemplary embodiment, first ANR may also include accelerations or decelerations of vehicle  200  that may be required to help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230  of predetermined road model trajectory  4212  in a predetermined amount of time. 
     Process  4300  may also include a step  4312  of adjusting steering system  240  based on first ANR. Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger first ANR by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of angle  1 . Processing unit  110  may also execute instructions stored in navigational response module  408  to control throttling system  220  and/or braking system  230  to appropriately control a speed of vehicle  200  to help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230  of predetermined road model trajectory  4212  in a predetermined amount of time. 
     Returning to step  4306 , when processing unit  110  determines that vehicle  200  is not located on road segment  4204  (Step  4306 : No), processing unit  110  may proceed to step  4314  via process segment C. In step  4314 , processing unit  110  may determine whether vehicle  200  is located in road segment  4206 . Processing unit  110  may perform operations similar to those discussed above in step  4306  to determine whether vehicle  200  is located in road segment  4206 . When processing unit  110  determines that vehicle  200  is not located in road segment  4206 , process  4300  may end. When processing unit  110  determines, however, that vehicle  200  is located in road segment  4206 , processing unit  100  may proceed to step  4316  of determining second ANR. 
     Processing unit  110  may determine second ANR using processes similar to those discussed above with respect to step  4310 . For example, processing unit  110  may determine a direction  4250  of predetermined road model trajectory  4226  at current location  4214  of vehicle  200 , a heading direction  4260 , and an angle of rotation  2 , which may help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250 . Further, like first ANR, second ANR may also include accelerations or decelerations of vehicle  200  that may be required to help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250  of predetermined road model trajectory  4226  in a predetermined amount of time. 
     Process  4300  may also include a step  4318  of adjusting steering system  240  based on second ANR. Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger second ANR by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of angle  2 . Processing unit  110  may also execute instructions stored in navigational response module  408  to control throttling system  220  and/or braking system  230  to appropriately control a speed of vehicle  200  to help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250  of predetermined road model trajectory  4226  in a predetermined amount of time. 
     Returning to step  4308 , when processing unit  110  determines that vehicle  200  is located on overlap segment  4220  (Step  4308 : Yes), processing unit  110  may proceed to step  4320  via process segment D. In step  4320 , processing unit  110  may determine first ANR. Processing unit  110  may determine first ANR using operations similar to those discussed above with respect to step  4310 . Thus, for example, processing unit may determine a direction  4240  of predetermined road model trajectory  4212  at current location  4214  of vehicle  200 , a heading direction  4230 , and an angle of rotation  1 , which may help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230 . Further, first ANR may also include accelerations or decelerations of vehicle  200  that may be required to help ensure that heading direction  4240  of vehicle  200  may be aligned with direction  4230  of predetermined road model trajectory  4212  in a predetermined amount of time. 
     Process  4300  may also include a step  4322  of determining a second ANR. Processing unit  110  may determine second ANR using operations similar to those discussed above with respect to step  4316 . Thus, for example, processing unit may determine a direction  4260  of predetermined road model trajectory  4226  at current location  4214  of vehicle  200 , a heading direction  4250 , and an angle of rotation  2 , which may help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250 . Further, second ANR may also include accelerations or decelerations of vehicle  200  that may be required to help ensure that heading direction  4260  of vehicle  200  may be aligned with direction  4250  of predetermined road model trajectory  4226  in a predetermined amount of time. 
     Process  4300  may also include a step  4324  of determining an error between first ANR and second ANR. In one exemplary embodiment, processing unit  110  may determine the error as an error between angles of rotation  1  and  2  determined, for example, in steps  4320  and  4322 . In another exemplary embodiment, processing unit  110  may determine the error as an error between direction  4230  of predetermined road model trajectory  4212  and direction  4250  of predetermined road model trajectory  4226 . In another exemplary embodiment, processing unit  110  may determine the error as a cosine distance between directions  4230  and  4250 . One of ordinary skill in the art would recognize that processing unit  110  may use other mathematical functions to determine the error between directions  4230  and  4250 . 
     Process  4300  may also include a step  4326  of determining whether the error is less than a threshold error. Because processing unit  110  may perform step  4324  only when vehicle  200  is located in overlap segment  4220 , the error may indicate whether the co-ordinate frame of vehicle  200  is aligned with both road segments  4204  and  4206 . It is contemplated that in some embodiments when vehicle  200  first enters overlap segment  4220 , the error may exceed the threshold error and navigating vehicle  200  based on both navigational maps  4200  and  4202  may improve accuracy. As vehicle  200  travels further within overlap segment  4220 , the error may decrease and may eventually become less than the threshold error. When the co-ordinate frame of vehicle  200  is aligned with both road segments  4204  and  4206 , the error may be smaller than the threshold error and it may be sufficient to start navigating vehicle  200  based only on navigational map  4202 . 
     When processing unit  110  determines that the error is greater than the threshold error (Step  4326 : Yes), processing unit  110  may proceed to step  4328 . In step  4328 , processing unit  110  may determine third ANR based on both the first ANR and the second ANR so that vehicle  200  may be navigated based on both maps  4200  and  4202 . Thus, for example, processing unit  110  may determine a third angle of rotation  3  as a combination of angles of rotation  1  and  2  determined, for example, in steps  4320  and  4322 . In some exemplary embodiments, the combination may be an average, a weighted average, or some other mathematical combination of angles of rotation  1  and  2 . Likewise, processing unit  110  may determine accelerations or decelerations for vehicle  200  based on a combination of the accelerations and/or decelerations determined, for example, in steps  4320  and  4322 . 
     Process  4300  may also include a step  4330  of adjusting steering system  240  based on third ANR. Processing unit  110  may be configured to execute instructions stored in navigational response module  408  to trigger third ANR by, for example, turning the steering wheel of vehicle  200  to achieve a rotation of angle  3 . Processing unit  110  may also execute instructions stored in navigational response module  408  to control throttling system  220  and/or braking system  230  based on the accelerations and/or decelerations determined in steps  4330  or  4332 . 
     Returning to step  4326 , when processing unit  110  determines that the error is less than the threshold error (Step  4326 : No), processing unit  110  may proceed to step  4332  of determining the third ANR based only on the second ANR. As discussed above, when the error is less than the threshold error, it may be sufficient to navigate vehicle  200  based only on map  4202 . Thus, in one exemplary embodiment, processing unit  110  may set third ANR equal to second ANR. In another exemplary embodiment, processing unit  110  may set third ANR by scaling (i.e. magnifying or attenuating) second ANR using a scaling factor. After completing step  4332 , processing unit  110  may proceed to step  4330  of adjusting steering system  240  based on third ANR. 
     Processing unit  110  and/or image acquisition unit  120  may repeat process  4300  after a predetermined amount of time. In one exemplary embodiment, the predetermined amount of time may range between about 0.5 seconds to 1.5 seconds. By repeatedly determining a current location  4214  of vehicle  200 , determining whether current location  4214  lies in overlap segment  4220 , and determining first ANR, second ANR, and third ANR based on the location of vehicle  200 , processing unit  110  and/or image acquisition unit  120  may help to navigate vehicle  200 , using overlapping road segment  4220  of local maps  4200 ,  4202 . 
     Sparse Map Autonomous Vehicle Navigation 
     In some embodiments, the disclosed systems and methods may use a sparse map for autonomous vehicle navigation. As discussed above regarding  FIGS. 8-11D , the sparse map may provide sufficient information for navigation without requiring excessive data storage or data transfer rates. Further, a vehicle (which may be an autonomous vehicle) may use the sparse map to navigate one or more roads. For example, as discussed below in further detail, vehicle  200  may determine an autonomous navigational response based on analysis of the sparse map and at least one image representative of an environment of vehicle  200 . 
     In some embodiments, vehicle  200  may access a sparse map that may include data related to a road on which vehicle  200  is traveling and potentially landmarks along the road that may be sufficient for vehicle navigation. As described in sections above, the sparse data maps accessed by vehicle  200  may require significantly less storage space and data transfer bandwidth as compared with digital maps including detailed map information, such as image data collected along a road. For example, rather than storing detailed representations of a road segment on which vehicle  200  is traveling, the sparse data map may store three dimensional polynomial representations of preferred vehicle paths along the road. A polynomial representation of a preferred vehicle path along the road may be a polynomial representation of a target trajectory along a road segment. These paths may require very little data storage space. 
     Consistent with disclosed embodiments, an autonomous vehicle system may use a sparse map for navigation. As discussed earlier, at the core of the sparse maps, one or more three-dimensional contours may represent predetermined trajectories that autonomous vehicles may traverse as they move along associated road segments. As also discussed earlier, the sparse maps may also include other features, such as one or more recognized landmarks, road signature profiles, and any other road-related features useful in navigating a vehicle. 
     In some embodiments, an autonomous vehicle may include a vehicle body and a processor configured to receive data included in a sparse map and generate navigational instructions for navigating the vehicle along a road segment based on the data in the sparse map. 
     As discussed above in connection with  FIG. 8 , vehicle  200  (which may be an autonomous vehicle) may access sparse map  800  to navigate. As shown in  FIG. 8 , in some embodiments, sparse map  800  may be stored in a memory, such as memory  140  or  150 . For example, sparse map  800  may be stored on a storage device or a non-transitory computer-readable medium provided onboard vehicle  200  (e.g., a storage device included in a navigation system onboard vehicle  200 ). A processor (e.g., processing unit  110 ) provided on vehicle  200  may access sparse map  4400  stored in the storage device or computer-readable medium provided onboard vehicle  200  in order to generate navigational instructions for guiding the autonomous vehicle  200  as it traverses a road segment. 
     In some embodiments, sparse map  800  may be stored remotely.  FIG. 44  shows an example of vehicle  200  receiving data from a remote server  4400 , consistent with disclosed embodiments. As shown in  FIG. 44 , remote server  4400  may include a storage device  4405  (e.g., a computer-readable medium) provided on remote server  4400  that communicates with vehicle  200 . For example, remote server  4400  may store a sparse map database  4410  in storage device  4405 . Sparse map database  4410  may include sparse map  800 . In some embodiments, sparse map database  4410  may include a plurality of sparse maps. Sparse map database  4410  may be indexed based on certain regions (e.g., based on geographical boundaries, country boundaries, state boundaries, etc.) or based on any appropriate parameter (e.g., type or size of vehicle, climate, etc.). Vehicle  200  may communicate with remote server  440  via one or more networks (e.g., over a cellular network and/or the Internet, etc.) through a wireless communication path. In some embodiments, a processor (e.g., processing unit  110 ) provided on vehicle  200  may receive data included in sparse map database  4410  over one or more networks from remove server  4400 . Furthermore, vehicle  200  may execute instructions for navigating vehicle  200  using sparse map  800 , as discussed below in further detail. 
     As discussed above in reference to  FIG. 8 , sparse map  800  may include representations of a plurality of target trajectories  810  for guiding autonomous driving or navigation along a road segment. Such target trajectories may be stored as three-dimensional splines. The target trajectories stored in sparse map  800  may be determined based on two or more reconstructed trajectories of prior traversals of vehicles along a particular road segment. A road segment may be associated with a single target trajectory or multiple target trajectories. For example, on a two lane road, a first target trajectory may be stored to represent an intended path of travel along the road in a first direction, and a second target trajectory may be stored to represent an intended path of travel along the road in another direction (e.g., opposite to the first direction). Additional target trajectories may be stored with respect to a particular road segment. 
     Sparse map  800  may also include data relating to a plurality of predetermined landmarks  820  associated with particular road segments, local maps, etc. As discussed in detail in other sections, these landmarks may be used in navigation of vehicle  200 . For example, in some embodiments, the landmarks may be used to determine a current position of vehicle  200  relative to a stored target trajectory. With this position information, vehicle  200  may be able to adjust a heading direction to match a direction of the target trajectory at the determined location. 
     Landmarks may include, for example, any identifiable, fixed object in an environment of at least one road segment or any observable characteristic associated with a particular section of a particular road segment. In some cases, landmarks may include traffic signs (e.g., speed limit signs, hazard signs, etc.). In other cases, landmarks may include road characteristic profiles associated with a particular section of a road segment. Further examples of various types of landmarks are discussed in previous sections, and some landmark examples are shown and discussed above in connection with  FIG. 10 . 
       FIG. 45  shows vehicle  200  navigating along a multi-lane road consistent with disclosed embodiments. Here, a vehicle  200  may navigate road segments present within a geographic region  1111  shown previously in  FIG. 11B . As previously discussed in relation to  FIG. 11B , road segment  1120  may include a multilane road with lanes  1122  and  1124 , double yellow line  1123 , and branching road segment  1130  that intersects with road segment  1120 . Geographic region  1111  may also include other road features, such as a stop line  1132 , a stop sign  1134 , a speed limit sign  1136 , and a hazard sign  1138 . 
       FIG. 46  shows vehicle  200  navigating using target trajectories along a multi-lane road consistent with disclosed embodiments. A vehicle  200  may navigate geographic region  1111  shown previously in  FIG. 11B  and  FIG. 45 , using target trajectory  4600 . Target trajectory  4600  may be included in a local map (e.g., local map  1140  of  FIG. 11C ) of sparse map  800 , and may provide a target trajectory for one or more lanes associated with a road segment. As previously discussed, sparse map  800  may include representations of road-related features associated with geographic region  1111 , such as representations of one or more landmarks identified in geographic region  1111 . Such landmarks may include speed limit sign  1136  and hazard sign  1138 . Vehicle  200  may use speed limit sign  1136  and hazard sign  1138  to assist in determining its current location relative to target trajectory  4600 . Based on the determined current location of vehicle  200  relative to target trajectory  4600 , vehicle  200  may adjust its heading to match a direction of the target trajectory at the determined location. 
     As discussed above, in some embodiments, sparse may  800  may also include road signature profiles. Such road signature profiles may be associated with any discernible/measurable variation in at least one parameter associated with a road. For example, in some cases, such profiles may be associated with variations in surface roughness of a particular road segment, variations in road width over a particular road segment, variations in distances between dashed lines painted along a particular road segment, variations in road curvature along a particular road segment, etc. 
       FIG. 47  shows an example of a road signature profile  1160  associated with vehicle  200  as it travels on the road shown in  FIGS. 45 and 46 . While profile  1160  may represent any of the parameters mentioned above, or others, in relation to vehicle  200 , in one example, profile  1160  may represent a measure of road surface roughness obtained by monitoring one or more sensors providing outputs indicative of an amount of suspension displacement as a vehicle  200  travels a road segment in  FIG. 46 . Alternatively, profile  1160  may represent variation in road width, as determined based on image data obtained via a camera onboard vehicle  200  traveling in a road segment in  FIG. 46 . Such profiles may be useful, for example, in determining a particular location of vehicle  200  relative to target trajectory  4600 , and may aid in navigation of vehicle  200 . That is, as vehicle  200  traverses a road segment of  FIG. 46 , vehicle  200  may measure a profile associated with one or more parameters associated with that road segment. If the measured profile can be correlated/matched with a predetermined profile that plots the parameter variation with respect to position along the road segment, then the measured and predetermined profiles may be used by vehicle  200  (e.g., by overlaying corresponding sections of the measured and predetermined profiles) in order to determine a current position along the road segment and, therefore, a current position relative to target trajectory  4600  for the road segment. Measurements of the profile by vehicle  200  may continue as vehicle  200  travels in lane  1124  of  FIG. 46  in order to continuously determine a current position along the road segment and a current position of vehicle  200  relative to target trajectory  4600 . As such, navigation of vehicle  200  may be provided. 
       FIG. 48  is an illustration of an example of a portion of a road environment  4800 , as shown in  FIGS. 45 and 46 . In this example,  FIG. 48  shows road segment  1120 . Vehicle  200  may be traveling along road segment  1120 . Along the road segment  1120 , landmarks such as speed limit sign  1136  and hazard sign  1138  may be present. Speed limit sign  1136  and hazard sign  1138  may be recognized landmarks that are stored in sparse map  800 , and may be used for autonomous vehicle navigation along road segment  1120  (e.g., for locating vehicle  200 , and/or for determining a target trajectory of vehicle  200 ). Recognized landmarks  1136  and  1138  in sparse map  800  may be spaced apart from each other at a certain rate. For example, recognized landmarks may be spaced apart in the sparse map at a rate of no more than 0.5 per kilometer, at a rate of no more than 1 per kilometer, or at a rate of no more than 1 per 100 meters. Landmarks  1136  and  1138  may be used, for example, to assist vehicle  200  in determining its current location relative to target trajectory  4600 , such that the vehicle may adjust its heading to match a direction of the target trajectory at the determined location. 
       FIG. 49  is a flow chart showing an exemplary process  4900  for sparse map autonomous navigation consistent with the disclosed embodiments. Processing unit  110  may utilize one of or both of application processor  180  and image processor  190  to implement process  4900 . As discussed below in further detail, vehicle  200  may determine an autonomous navigational response based on analysis of a sparse map and at least one image representative of an environment of vehicle  200 . 
     At step  4902 , processing unit  110  may receive a sparse map of a road segment, such as sparse map  800 , from memory  140  or  150 . For example, the sparse map may be transmitted to processing unit  110  based on a calculation of the position of vehicle  200  by position sensor  130 . In other exemplary embodiments, vehicle  200  may receive the sparse map from remote server  4400 . The sparse map data may have a particular data density. The data density of the sparse map may be expressed in terms of data unit per unit distance. For example, the sparse map may have a data density of no more than 1 megabyte per kilometer. In another example, the sparse map may have a data density of no more than 100 kilobytes per kilometer. In another example, the sparse map may have a data density of no more than 10 kilobytes per kilometer. Data density may be expressed in terms of any conceivable data unit and unit distance. Further, the sparse map may include a polynomial representation of a target trajectory along the road segment. 
     At step  4904 , processing unit  110  may receive at least one image representative of an environment of vehicle  200 . For example, processing unit  110  may receive at least one image from image acquisition unit  120  using image capture device  122 . In other exemplary embodiments, image acquisition unit  120  may acquire one or more images from one or more of image capture devices  122 ,  124 , and  126 . Image acquisition unit  120  may transmit the one or more images to processing unit  110  over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). 
     At step  4906 , processing unit  110  may analyze the received sparse map and the at least one image of the environment of vehicle  200 . For example, processing unit  110  may execute monocular image analysis module  402  to analyze one or more images, as described in further detail in connection with  FIGS. 5B-5D . By performing the analysis, processing unit  110  may detect a set of features within the set of images, for example, one or more landmarks, such as landmarks  1134 ,  1136 , and  1138 . As discussed earlier, landmarks may include one or more traffic signs, arrow markings, lane markings, dashed lane markings, traffic lights, stop lines, directional signs, reflectors, landmark beacons, lampposts, a change is spacing of lines on the road, signs for businesses, and the like. Furthermore, processing unit  110  may analyze the sparse map to determine that an object in one or more images is a recognized landmark. For example, processing unit  110  may compare the image of the object to data stored in the sparse map. Based on the comparison, the image processor  190  may determine whether or not the object is a recognized landmark. Processing unit  110  may use recognized landmarks from captured image data of the environment and/or GPS data to determine a position of vehicle  200 . Processing unit  110  may then determine a position of vehicle  200  relative to a target trajectory of the sparse map. 
     At step  4908 , processing unit  110  may cause one or more navigational responses in vehicle  200  based solely on the analysis of the sparse map and at least one image of the environment performed at step  4906 . For example, processing unit  110  may select an appropriate navigational response based on the position of vehicle  200  relative to the target trajectory of the sparse map. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. Processing unit  110  may cause system  100  to provide inputs (e.g., control signals) to one or more of throttling system  220 , braking system  230 , and steering system  240  as shown in  FIG. 2F  to navigate vehicle  200  (e.g., by causing an acceleration, a turn, a lane shift, etc.) to provide a navigational response. System  100  may provide inputs to one or more of throttling system  220 , braking system  230 , and steering system  240  over one or more data links (e.g., any wired and/or wireless link or links for transmitting data). Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof. For instance, processing unit  110  may cause vehicle  200  to shift one lane over and then accelerate by, for example, sequentially transmitting control signals to steering system  240  and throttling system  220  of vehicle  200 . Alternatively, processing unit  110  may cause vehicle  200  to brake while at the same time shifting lanes by, for example, simultaneously transmitting control signals to braking system  230  and steering system  240  of vehicle  200 . 
     Navigation Based on Expected Landmark Location 
     Landmarks appearing in one or more images captured by a camera onboard a vehicle may be used in the disclosed embodiments to determine a location of a vehicle along a road model trajectory. Such landmarks may include recognized landmarks represented, for example, in sparse map  800 . Processing unit  110  of vehicle  200  may analyze images captured from one or more cameras onboard vehicle  200  to look for and verify the presence of a recognized landmark (from sparse data map  800 ) in the captured images. According to techniques described in detail in other sections of the disclosure, the verified, recognized landmarks in the environment of the vehicle can then be used to navigate the vehicle (e.g., by enabling a determination of a position of vehicle  200  along a target trajectory associated with a road segment). 
     In the disclosed embodiments, however, processor unit  110  may also generate navigational instructions on not only those landmarks appearing in captured images, but also based on an expected location of the recognized landmark as conveyed by sparse data map  800 . For example, braking of a vehicle may be initiated a certain distance from recognized landmarks such as a stop line, a traffic light, a stop sign, a sharp curve, etc., even before those landmarks are detectable via an on-board camera. Landmarks may include, for example, any identifiable, fixed object in an environment of at least one road segment or any observable characteristic associated with a particular section of the road segment. In some cases, landmarks may include traffic signs (e.g., speed limit signs, hazard signs, etc.). In other cases, landmarks may include road characteristic profiles associated with a particular section of a road segment. Further examples of various types of landmarks are discussed in previous sections, and some landmark examples are shown in  FIG. 10 . 
       FIG. 50  illustrates an example environment consistent with the disclosed embodiments. Vehicle  200  (which may be an autonomous vehicle) may travel along a target road model trajectory  5002  in road  5004 . Vehicle  200  may be equipped with one or more image capture devices (e.g., one or more of image capture device  122 ,  124 , or  126 ) that capture an image of the environment of the vehicle. The one or more image capture devices may have a sight range  5006 . Sight range  5006  may define a range at which an image capture device of vehicle  200  can capture accurate images of the environment around vehicle  200 . For example, sight range  5006  may define the range at which the field of view, focal length, resolution focus, sharpness, image quality, and the like of the image capture device of vehicle  200  is sufficient to provide images for navigation of vehicle  200 . Region  5008  may define a range outside of the sight range  5006  of an image capture device of vehicle  200 . In region  5008 , an image capture device of vehicle  200  may not be able to capture images of the environment around vehicle  200  that are sufficient to allow navigation of vehicle  200 . In other exemplary embodiments, each image capture device may have a different sight range. 
     As shown in  FIG. 50 , recognized landmark  5010  is within sight range  5006 . Because recognized landmark  5010  is within sight range  5006 , it may be captured by an image capture device of vehicle  200  and identified, and used to navigate vehicle  200 . Recognized landmark  5010  may be identified by vehicle  200  according to techniques discussed above in connection with, for example,  FIGS. 34-36 . 
     As previously discussed, recognized landmark  5012  is within region  5008 . However, region  5008  defines a range outside of the sight range  5006  of an image capture device of vehicle  200 . Accordingly, vehicle  200  may not be able to identify recognized landmark  5012  using an image capture device of vehicle  200  because recognized landmark  5012  is out of the sight range of the image capture device. 
     Consistent with disclosed embodiments, vehicle  200  may identify recognized landmark  5012  using alternative techniques. For example, an image capture device of vehicle  200  may capture an image of the environment within sight range  5006 . A processor of vehicle  200  (e.g., processing unit  110 ) may receive the image. The processor may then determine a position of vehicle  200  along predetermined road model trajectory  5002  in road  5004  based on the captured image. For example, as discussed in other sections, the processor may compare data information representing recognized landmark  5010  from the captured image of the environment to stored data, such as data stored in sparse map  800 , discussed above, to determine a position of vehicle  200  along predetermined road model trajectory  5002  in road  5004 . 
     Based on the determined position of vehicle  200 , the processor may then identify a recognized landmark beyond sight range  5006  (e.g., recognized landmark  5012 ) forward of the vehicle  200 . For example, by accessing information stored in sparse data map  800  or any portion of sparse data map  800  (e.g., any received local map portions of sparse data map  800 ) processing unit  110  of vehicle  200  may determine the next expected recognized landmark to be encountered by vehicle  200  (or any other recognized landmark to be encountered by vehicle  200 ). The processor may also determine a predetermined position of recognized landmark  5012  based on the information available in sparse data map  800 . Then, processing unit  110  may determine a current distance  5014  between the vehicle  200  and expected, recognized landmark  5012 . The current distance  5014  between the vehicle  200  and the recognized landmark  5012  may be determined by comparing the determined position of vehicle  200  with the predetermined position of recognized landmark  5012 . Based on the distance  5014 , the processor of vehicle  200  may then determine an autonomous navigational response for the vehicle. For example, among other responses, processing unit  110  may initiate braking in advance of landmark  5012  even prior to detection of landmark  5012  in any captured images from image capture devices onboard vehicle  200 . 
       FIG. 51  illustrates a configuration  5100  for autonomous navigation consistent with disclosed embodiments. As discussed earlier, processing unit  110  may receive images from an image acquisition unit  120 . Image acquisition unit may include one or more image capture devices (e.g., image capture device  122 ,  124 , or  126 ). The images may depict an environment of vehicle  200  within the field of view of an image capture device onboard vehicle  200 . 
     While GPS data need not be relied upon to determine an accurate position of vehicle  200  along a target trajectory, GPS data (e.g., GPS data from GPS unit  5106 ) may be used as an index for determining relevant local maps to access from within sparse data map  800 . Such GPS data may also be used as a general index to aid in verifying an observed recognized landmark. 
       FIG. 52  shows an example of an environment  5200  consistent with the present disclosure. As shown in  FIG. 52 , a vehicle  200  may approach a junction  5202  with a stop sign  5204  and a stop line  5210 . One or both of stop sign  5204  or stop line  5210  may correspond to recognized landmarks represented in sparse data map  800 . Either or both of stop sign  5204  or stop line  5210  may be located in a region  5208  beyond a focal length of an image capture device aboard vehicle  200  or otherwise outside of a usable sight range of the image capture device. Based on information stored in sparse data map  800  relative to stop sign  56204  and/or stop line  5210 , processing unit  110  may initiate braking based on a determined, expected distance to stop line  5204  or stop line  5210  even before stop line  5204  or  5210  have been identified in images received from the image capture device onboard vehicle  200 . Such a navigation technique, for example, may aid in slowing vehicle  200  gradually or according to a predetermined braking profile even without visual confirmation of a distance to a trigger for braking (e.g., stop sign  5204 , stop line  5210 , an expected curve, etc.). 
       FIG. 53  shows another example environment  5300  consistent with the present disclosure. As shown in  FIG. 53 , a vehicle  200  may approach a curve  5302  of road  5304 . Vehicle  200  may include an image acquisition unit (e.g., image acquisition unit  120 ) including one or more image capture devices that provide a sight range of  5306 . Region  5308  may define a range outside of the sight range  5306  of the image acquisition unit of vehicle  200 . 
     Vehicle  200  may need to slow down in speed or implement steering to account for curve  5302  in road  5304 . To plan a slowdown in speed or implement steering, it may be useful to know in advance where the curve  5302  is located. However, curve  5302  may be located in region  5308 , which is beyond the focal length of an image capture device aboard vehicle  200 . Thus, vehicle  200  may use a predetermined position of curve  5302 , for example, as represented in sparse data map  800 , as well as the position of vehicle  200  along predetermined road model trajectory  5310 , to determine a distance  5320  to curve  5302 . This distance may be used to slow vehicle  200  change a course of vehicle  200 , etc. before the curve appears in images captured by an onboard camera. 
     Consistent with disclosed embodiments, to determine distance  5320  to curve  5302 , the image acquisition device of vehicle  200  may capture an image of the environment. The image may include a recognized landmark  5318 . A processor of vehicle  200  (e.g., processing unit  110 ) may receive the image and determine a position of vehicle  200  along predetermined road model trajectory  5310  based on the captured image and the position of recognized landmark  5318 . Based on the determined position of vehicle  200 , the processor may then identify curve  5302  beyond sight range  5306  forward of the vehicle  200  based on information included in sparse data map  800  relevant to curve  5302 . Position information included in sparse data map  800  for curve  5302  may be compared with a determined position for vehicle  200  along a target trajectory for vehicle  200  to determine a distance between vehicle  200  and curve  5302 . This distance can be used in generating a navigational response for vehicle  200  prior to identification of curve  5302  within images captured by a camera onboard vehicle  200 . 
       FIG. 54  is a flow chart showing an exemplary process  5400  for autonomously navigating vehicle  200  consistent with the disclosed embodiments. A processing unit (e.g., processing unit  110 ) of vehicle  200  may use one of or both of application processor  180  and image processor  190  to implement process  5400 . As discussed below in further detail, vehicle  200  may autonomously navigate along a road segment based on a predetermined landmark location. Furthermore, the predetermined landmark location may be beyond a sight range of vehicle  200 . 
     At step  5402 , a processing unit (e.g., processing unit  110 ) of vehicle  200  may receive at least one image from an image capture device (e.g., image capture device  122 ) of vehicle  200 . The at least one image may be representative of an environment of vehicle  200 . The at least one image may include data representative of one or more landmarks in the environment. For example, the at least one image may include data representative of landmarks such as road signs (including stop signs, yield signs, and the like), traffic lights, general signs, lines on the road, and curves along a road segment. As discussed in previous sections, a processing unit of vehicle  200  may verify recognized landmarks that appear in the at least one image. 
     At step  5404 , the processing unit of vehicle  200  may determine a position of vehicle  200 . For example, the processing unit of vehicle  200  may determine a position of vehicle  200  along a predetermined road model trajectory associated with a road segment based, at least in part, on information associated with the at least one image. 
     At step  5406 , a recognized landmark beyond the focal range of the image capture device of vehicle  200  and forward of vehicle  200  may be identified. The identification may be based on the determined position of vehicle  200  along the predetermined road model trajectory associated with the road segment. For example, information about recognized landmarks along a predetermined road model trajectory may be previously stored in a sparse map, such as sparse map  800 , discussed above. Based on the determined position of vehicle  200  along the predetermined road model trajectory associated with the road segment, the processing unit of vehicle  200  may determine that one or more recognized landmarks are located forward of vehicle  200  along the predetermined road model trajectory, but beyond a sight range of the image capture device of vehicle  200 . Moreover, the processing unit of vehicle  200  may access a predetermined position of the recognized landmarks by accessing sparse map  800 . 
     At step  5408 , a current distance between the vehicle and the recognized landmark located forward of vehicle  200  beyond a sight range of the image capture device of vehicle  200  may be determined. The current distance may be determined by comparing the determined position of vehicle  200  along the predetermined road model trajectory associated with the road segment to the predetermined position of the recognized landmark forward of vehicle  200  beyond the sight range. 
     At step  5410 , an autonomous navigational response for the vehicle may be determined based on the determined current distance between vehicle  200  and the recognized landmark located forward of vehicle  200 . Processing unit  110  may control one or more of throttling system  220 , braking system  230 , and steering system  240  to perform a certain navigational response, as discussed in other sections of this disclosure. For example, an autonomous navigational response may include sending a control signal to braking system  230  to provide the application of brakes associated with vehicle  200 . In another example, an autonomous navigational response may include sending a control signal to steering system  240  to modify a steering angle of vehicle  200 . 
     Autonomous Navigation Based on Road Signatures 
     Consistent with disclosed embodiments, the system may navigate based on predetermined road signatures without using landmarks. As discussed above, such road signatures may be associated with any discernible or measurable variation in at least one parameter associated with a road. 
     For example, in some cases, road signatures may be associated with variations in surface roughness of a particular road segment, variations in road width over a particular road segment, variations in distances between dashed lines painted along a particular road segment, variations in road curvature along a particular road segment, etc. The road signatures may be identified as a vehicle traverses a road segment based on visual information (e.g., images obtained from a camera) or based on other sensor output (e.g., one or more suspension sensor outputs, accelerometers, etc.). These signatures may be used to locate the vehicle along a predetermined road profile, and the forward trajectory can then be determined for the vehicle based on the direction of the road model at the determined location compared to a heading direction for the vehicle. 
       FIG. 55  is a diagrammatic representation of exemplary vehicle control systems, consistent with the disclosed embodiments. As illustrated in  FIG. 55 , vehicle  200  (which may be an autonomous vehicle) may include processing unit  110 , which may have features similar to those discussed above with respect to  FIGS. 1 and 2F . Vehicle  200  may also include imaging unit  220 , which may also have features similar to those discussed above with respect to  FIGS. 1 and 2F . In addition, vehicle  200  may include one or more suspension sensors  5500  capable of detecting movement of the suspension of vehicle  200  relative to a road surface. For example, signals from suspension sensors  5500  located adjacent each wheel of vehicle  200  may be used to determine a local shape, inclination, or banking of the road surface over which vehicle  200  may be located. In some exemplary embodiments, vehicle  200  may additionally or alternatively include accelerometers or other position sensors that may acquire information regarding variations in the road surface as vehicle  200  travels over the road surface. It is also contemplated that system  100  illustrated in  FIG. 55  may include some or all of the components described above with respect to, for example,  FIGS. 1 and 2F . 
       FIG. 56  illustrates vehicle  200  traveling on road segment  5600  in which the disclosed systems and methods for navigating vehicle  200  using one or more road signatures may be used. Road segment  5600  may include lanes  5602  and  5604 . As illustrated in  FIG. 56 , lane  5602  may be delimited by road center  5606  and right side  5608 , whereas lane  5604  may be delimited by left side  5610  and road center  5606 . Lanes  5602  and  5604  may have the same or different widths. It is also contemplated that each of lanes  5602 ,  5604  may have uniform or non-uniform widths along a length of road segment  5600 . Although  FIG. 56  depicts road segment  5600  as including only two lanes  5602 ,  5604 , it is contemplated that road segment  5600  may include any number of lanes. 
     In one exemplary embodiment as illustrated in  FIG. 56 , vehicle  200  may travel along lane  5602 . Vehicle  200  may be configured to travel along predetermined road model trajectory  5612 , which may define a preferred path (e.g., a target road model trajectory) within lane  5602  of road segment  5600 . In some exemplary embodiments, predetermined road model trajectory  5612  may be located equidistant from road center  5606  and right side  5608 . It is contemplated however that predetermined road model trajectory  5612  may be located nearer to one or the other of center  5606  and right side  5608  of road segment  5600 . In some embodiments, road model trajectory  5612  may be located elsewhere with respect to the road. For example, road model trajectory  5612  may be located to approximately coincide with a center of a roadway, a road edge, a lane edge, etc. 
     In some embodiments, predetermined road model trajectory  5612  may be mathematically represented by a three-dimensional polynomial function, which may be stored in memories  140 ,  150  associated with vehicle  200 . It is also contemplated that the three-dimensional polynomial representation of road model trajectory  5612  may be stored in a storage device located remotely from vehicle  200 . Processing unit  110  of vehicle  200  may be configured to retrieve predetermined road model trajectory  5612  from storage device over a wireless communications interface. 
     Vehicle  200  may be equipped with image capture devices  122 ,  124 ,  126  of image acquisition unit  120 . It is contemplated that vehicle  200  may include more or fewer image capture devices than those shown in  FIG. 56 . Image capture devices  122 ,  124 ,  126  may be configured to acquire a plurality of images representative of an environment of vehicle  200 , as vehicle  200  travels along road segment  5600 . For example, one or more of image capture devices  122 ,  124 ,  126  may obtain the plurality of images showing views forward of vehicle  200 . Processing unit  110  of vehicle  200  may be configured to detect a location of vehicle  200  at vehicle travels along road segment  5600  based on the one or more images obtained by image capture devices  122 ,  124 ,  126  or based on signals received from, for example, suspension sensor  5500 . 
     As illustrated in  FIG. 56 , vehicle  200  may travel via locations  5622 ,  5624 ,  5626 , to current location  5628 . Although only three prior locations  5622 - 5626  are illustrated in  FIG. 56 , one of ordinary skill in the art would recognize that any number of previous locations of vehicle  200  may be present on road segment  5600 . Processing unit  110  may analyze the one or more images received from image capture devices  122 ,  124 ,  126 , to determine, for example, road widths W p1 , W p2 , W p3 , W c  at locations  5622 ,  5624 ,  5626 ,  5628 , respectively, where the subscript “p” refers to a previous location and the subscript “c” refers to current location  5628  of vehicle  200 . In some exemplary embodiments, processing unit  110  may additionally or alternatively determine, for example, lane widths D p1 , D p2 , D p3 , D c  at locations  5622 ,  5624 ,  5626 ,  5628 , respectively. Processing unit  110  may generate a road width profile or a lane width profile over portion  5614  of road segment  5600 . The determined road width profile or lane width profile may correspond to current location  5628 . 
       FIG. 57  illustrates an exemplary profile  5700  generated by processing unit  110  of vehicle  200 . As illustrated in  FIG. 57 , road width, lane width, or other parameters may be charted on the y-axis against a distance traveled by vehicle  200  along road segment  5600  on the x-axis. Processing unit  110  may determine the distance traveled using systems and methods similar to those discussed above with respect to  FIGS. 34-36 . 
     Processing unit  110  may determine a local feature of road segment  5600  corresponding to current location  5628  of vehicle  200 . For example, processing unit  110  may determine a mathematical representation for the profile (e.g. profile shown in  FIG. 57 ) by curve fitting the determined road widths W p1 , W p2 , W p3 , W c  and/or lane widths D p1 , D p2 , D p3 , D c . In one exemplary embodiment, processing unit  110  may determine, for example, coefficients (e.g. a 1 , a 2 , . . . a n ) associated with the curve fit of the road width profile or the lane width profile. The determined coefficients may represent the local feature of road segment  5600  at current location  5628 . In another exemplary embodiment, processing unit  110  may determine a slope of profile  5700  as the local feature. It is contemplated that processing unit  110  may perform other mathematical operations on profile  5700  to determine the local feature of road segment  5600  corresponding to a current location of vehicle  200 . 
     Processing unit  110  may retrieve a predetermined signature feature associated with road segment  5600 , for example, from database  160  stored in memories  140 ,  150 . In one exemplary embodiment, the predetermined signature features may include coefficients of best or preferred fit lines representing road width profiles or lane width profiles corresponding to various locations along predetermined road model trajectory  5612 . For example, the predetermined signature features may include coefficients b 1 , b 2 , . . . b n  at location  1 ; c 1 , c 2 , . . . c n  at location  2 ; d 1 , d 2 , . . . d n  at location  3 , etc. Processing unit  110  may compare the coefficients (e.g. a 1 , a 2 , . . . a n ) determined based on road widths W p1 , W p2 , W p3 , W c  and/or lane widths D p1 , D p2 , D p3 , D c  with the coefficients (e.g. b 1 , b 2 , . . . b n ; c 1 , c 2 , . . . c n ; d 1 , d 2 , . . . d n ; etc.). Processing unit  110  may determine current location  5628  of vehicle  200  based on a match of the coefficients. For example, if coefficients a 1 , a 2 , . . . a n  match with coefficients c 1 , c 2 , . . . c n , respectively, processing unit  110  may determine location  5628  of vehicle  200  as corresponding to location  2  of predetermined road model trajectory  5612 . 
     Processing unit  110  may determine a match in many ways. In one exemplary embodiment, processing unit  110  may determine a distance measure between the coefficients (e.g. a 1 , a 2 , . . . a n ) and each set of coefficients (e.g. b 1 , b 2 , . . . b n ; c 1 , c 2 , . . . c n ; d 1 , d 2 , . . . d n ; etc.) corresponding to locations  1 ,  2 ,  3 , etc. Processing unit  110  may determine that there is a match when at least one of the determined distance measures is less than a threshold distance. In other exemplary embodiments, processing unit  110  may determine an error between the coefficients (e.g. a 1 , a 2 , . . . a n ) and each set of coefficients (e.g. b 1 , b 2 , . . . b n ; c 1 , c 2 , . . . c n ; d 1 , d 2 , . . . d n ; etc.) corresponding to locations  1 ,  2 ,  3 , etc. Processing unit  110  may determine a match when at least one error is less than a threshold error. One of ordinary skill in the art would recognize that processing unit  110  may use other mathematical computations to determine a correlation or match between the two sets of coefficients. 
     In some exemplary embodiments, processing unit  110  may use road width W 4  and/or lane width w 4  as local feature to determine current location  5628  of vehicle  500 . For example, the predetermined signature features of road segment  5600  may include road widths w 1 , w 2 , w 3 , w 4 , w 5 , . . . w n  corresponding to locations  1 ,  2 ,  3 ,  4 ,  5 , . . . n along predetermined road model trajectory  5612 . Additionally, or alternatively, the predetermined signature features of road segment  5600  may include lane widths d 1 , d 2 , d 3 , d 4 , d 5 , . . . d n  corresponding to locations locations  1 ,  2 ,  3 ,  4 ,  5 , . . . n along predetermined road model trajectory  5612 . Processing unit  110  may compare road width W c  and/or lane width D c  with road widths w 1 , w 2 , w 3 , w 4 , w 5 , . . . w n  and/or lane widths d 1 , d 2 , d 3 , d 4 , d 5 , . . . d n , respectively, to determine current location  5628 . For example, if road width W c  matches with road width w 5 , processing unit  110  may determine location  5628  as corresponding to location  5 . Likewise, if lane width D c  matches with lane width d 3 , processing unit  110  may determine location  5628  as corresponding to location  3 . Processing unit may determine whether road width W 4  and/or lane width D 4  and match using matching techniques similar to those discussed above. 
     Processing unit  110  may use other parameters to determine current location  5628 . For example, processing unit  110  may determine one or more of average road width W avg  (e.g. average of W p1 , W p2 , W p3 , W c ), road width variance W var  (e.g. variance of W p1 , W p2 , W p3 , W c ), average lane width D avg  (e.g. average of D p1 , D p2 , D p3 , D c ), lane width variance D var  (e.g. variance of D p1 , D p2 , D p3 , D c ), or other parameters such as median, mode, etc. to represent the local feature corresponding to current location  5628 . The corresponding predetermined road signature feature may also be represented by average road widths, road width variances, average lane widths, lane width variances, median or mode values of road widths, median or mode values of lane widths, etc., at predetermined locations on predetermined road model trajectory  5612 . Processing unit  110  may determine current location  5628  of vehicle  200  by comparing the determined local feature and the predetermined road signature features as discussed above. 
     In some exemplary embodiments, the local features and predetermined signature features of road segment  5600  may be based on lengths of, or spacing between, marks on road (road markings) segment  5600 .  FIG. 58  illustrates vehicle  200  traveling on road segment  5600  in which the predetermined road signatures may be based on road markings on road segment  5600 . For example,  FIG. 58  illustrates road center  5606  as a dashed line represented by road markings  5802 - 5816 . As vehicle  200  travels along road segment  5600 , processing unit  110  may analyze the one or more images received from the one or more image capture devices  122 ,  124 ,  126 , etc. to detect road markings  5802 - 5816 . Processing unit  110  may also determine, for example, spacings S p1 , S p2 , S p3 , S c  between road markings  5802 - 5804 ,  5804 - 5806 ,  5806 - 5808 ,  5808 - 5810 , respectively. Processing unit  110  may additionally or alternatively determine lengths L p1 , L p2 , L p3 , L c  for road markings  5802 ,  5804 ,  5806 ,  5808 , respectively. In one exemplary embodiment, processing unit may generate a dashed line spacing profile or a dashed line length profile based on spacings S p1 , S p2 , S p3 , S c  or lengths L p1 , L p2 , L p3 , L c , respectively, in a manner similar to the profiles discussed above with respect to  FIGS. 56 and 57 . Processing unit  110  may also determine a local feature based on coefficients of curve fits to the dashed line spacing profile and/or dashed line length profile as discussed above with respect to  FIGS. 56 and 57 . Processing unit  110  may compare the local feature (e.g., coefficients representing the dashed line spacing profile or dashed line length profile) to predetermined signature features of road segment  5600 . For example, processing unit  110  may compare the coefficients representing the determined dashed line spacing profile or dashed line length profile with predetermined coefficients of dashed line spacing/length profiles at known locations along predetermined road model trajectory. Processing unit  110  may determine current location  5628  of vehicle  200  when the coefficients of the determined dashed line spacing/length profiles match the predetermined coefficients at a particular known location as discussed above with respect to  FIGS. 56 and 57 . 
     In some exemplary embodiments, processing unit  110  may use dashed line spacing S c  and/or dashed line length L c  as a local feature to determine current location  5628  of vehicle  500 . For example, the predetermined signature features of road segment  5600  may include dashed lane spacings s 1 , s 2 , s 3 , s 4 , s 5 , . . . s n  corresponding to locations  1 ,  2 ,  3 ,  4 ,  5 , . . . n along predetermined road model trajectory  5612 . Additionally, or alternatively, the predetermined signature features of road segment  5600  may include dashed line lengths l 1 , l 2 , l 3 , l 4 , l 5 , . . . l n  corresponding to locations  1 ,  2 ,  3 ,  4 ,  5 , . . . n along predetermined road model trajectory  5612 . Processing unit  110  may compare dashed line spacing S c  and/or dashed line length L c  with dashed lane spacings s 1 , s 2 , s 3 , s 4 , s 5 , . . . s n  and/or dashed line lengths l 1 , l 2 , l 3 , l 4 , l 5 , . . . l n , respectively, to determine current location  5628 . For example, if dashed line spacing S c  matches with dashed line spacing s 5 , processing unit  110  may determine location  5628  as corresponding to location  5 . Likewise, if dashed line length L c  matches with dashed line length l 3 , processing unit  110  may determine location  5628  as corresponding to location  3 . Processing unit may determine whether dashed line spacing S c  and/or dashed lane length L c  match the predetermined dashed line lengths or spacings using matching techniques similar to those discussed above. 
     In other exemplary embodiments, processing unit  110  may determine an average dash line length L avg , dash line variance L var , dash line spacing average S avg , or dash line spacing variance S var  as a local parameter. Processing unit  110  may compare dash mark length L avg , dash mark variance L var , dash mark spacing average S avg , or dash mark spacing variance S var  with predetermined values of dash mark length, dash mark variance, dash mark spacing average, or dash mark spacing variance at various locations along predetermined road model trajectory  5612 . The predetermined values of dash mark length, dash mark variance, dash mark spacing average, or dash mark spacing variance at various locations may constitute predetermined signature features of road segment  5600 . Processing unit  110  may determine current location  5628  of vehicle  200  as the location for which at least one of dash mark length L avg , dash mark variance L var , dash mark spacing average S avg , or dash mark spacing variance S var  matches a predetermined corresponding value of dash mark length, dash mark variance, dash mark spacing average, or dash mark spacing. 
     In yet other exemplary embodiments, processing unit  110  may use a number of dashed lines as a local feature. For example, road markings  5802 - 5816  may be painted at a fixed length and spacing when they are painted by a machine. Thus, it may be possible to determine current location  5628  of vehicle  200  based on a count of the road markings as vehicle  200  travels on road segment  5600 . Processing unit  110  may determine a count “N c ” of dash marks that vehicle  200  may have passed till it reaches current location  5628 . Processing unit  110  may compare count N c  with counts n 1 , n 2 , n 3 , . . . n n  corresponding to the number of road markings up to locations  1 ,  2 ,  3 , . . . n, respectively, along predetermined road model trajectory  5612 . Counts n 1 , n 2 , n 3 , . . . n n  may correspond to the predetermined signature feature of road segment  5600 . In one example, when count N c  matches n 2 , processing unit may determine current location  5628  as corresponding to location  2 . 
     In some exemplary embodiments, the local features and predetermined signature features of road segment  5600  may be based on radii of curvature of the predetermined road model trajectory and an actual trajectory traveled by vehicle  200 . For example, as illustrated in  FIG. 59 , vehicle  200  may travel over road segment  5900 , which may include lanes  5902  and  5904 . As illustrated in  FIG. 59 , lane  5902  may be delimited by road center  5906  and right side  5908 , whereas lane  5904  may be delimited by left side  5910  and road center  5906 . Vehicle  200  may be configured to travel along predetermined road model trajectory  5912 , which may define a preferred path (e.g., a target road model trajectory) within lane  5902  of road segment  5900  that vehicle  200  may follow as vehicle  200  travels along road segment  5900 . As also illustrated in  FIG. 59 , vehicle  200  may travel via previous locations  5922 ,  5924 ,  5926 ,  5928 ,  5930 ,  5932  to current location  5934 . Although only six previous locations  5922 - 5932  are illustrated in  FIG. 59 , one of ordinary skill in the art would recognize that any number of previous locations of vehicle  200  may be present on road segment  5900 . 
     Processing unit  110  may determine traveled trajectory  5914  of vehicle  200  as passing through previous locations  5922 - 5932  of vehicle  200 . In one exemplary embodiment, processing unit  110  may fit a curve, which may be a three-dimensional polynomial similar to that representing predetermined road model trajectory  5912 , through locations  5922 - 5932 . Processing unit  110  may also determine first parameter values representative of curvatures of various segments (portions or sections) of predetermined road model trajectory  5912 . Further, processing unit  110  may determine second parameter values representative of a curvature of traveled trajectory  5914 . Processing unit  110  may determine current location  5934  of vehicle  200  based on the first and second parameter values. 
     For example, consider the case where R 1 , R 2 , R 3 , . . . R z  represent radii of curvature of segments C 1 , C 2 , C 3 , . . . C z  of predetermined road model trajectory  5912 . Referring to  FIG. 59 , portions C 1 , C 2 , C 3 , . . . C z  of predetermined road model trajectory  5912  may represent sections of predetermined road model trajectory  5912  between locations  5922 - 5944 ,  5922 - 5946 ,  5922 - 5948 , etc. Processing unit may determine, for example, a radius of curvature R t  of traveled trajectory  5914  between locations  5922  and  5934 . Processing unit  110  may compare radius of curvature R t  with the radii R 1 , R 2 , R 3 , . . . R z . Processing unit  110  may determine current location  5934  of vehicle  200  as a location  5970  when radius of curvature R t  matches the radius of curvature R p  of a portion of predetermined road model trajectory lying between location  5922  and  5970 . Processing unit  110  may determine a match between radii R t  and R p  using matching techniques similar to those discussed above. 
       FIG. 60  is a flowchart showing an exemplary process  6000 , for navigating vehicle  200  along road segment  5900  (or  5600 ), using road signatures, consistent with disclosed embodiments. Steps of process  6000  may be performed by one or more of processing unit  110  and image acquisition unit  120 , with or without the need to access memory  140  or  150 . The order and arrangement of steps in process  6000  is provided for purposes of illustration. As will be appreciated from this disclosure, modifications may be made to process  6000  by, for example, adding, combining, removing, and/or rearranging the steps for the process. 
     As illustrated in  FIG. 60 , process  6000  may include a step  6002  of receiving information regarding one or more aspects of road segment  5900  (or  5600 ) from a sensor. In one exemplary embodiment, sensor may include one or more of image capture devices  122 ,  124 ,  126 , which may acquire one or more images representative of an environment of the vehicle. In one exemplary embodiment, image acquisition unit  120  may acquire one or more images of an area forward of vehicle  200  (or to the sides or rear of a vehicle, for example). For example, image acquisition unit  120  may obtain an image using image capture device  122  having a field of view  202 . In other exemplary embodiments, image acquisition unit  120  may acquire images from one or more of image capture devices  122 ,  124 ,  126 , having fields of view  202 ,  204 ,  206 . Image acquisition unit  120  may transmit the one or more images to processing unit  110  over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). 
     In another exemplary embodiment, the sensor may include one or more suspension sensors  5500  on vehicle  200 . Suspension sensor  5500  may be configured to generate signals responsive to a movement of the suspension of vehicle  200  relative to a surface of road segment  5900  (or  5600 ). Processing unit  110  may receive signals from the one or more suspension sensors  5500  on vehicle  200  as vehicle  200  moves along road segment  5900  (or  5600 ). For example, processing unit  110  may receive information regarding the relative height of vehicle  200  adjacent each of its wheels based on suspension sensors  5500  located adjacent to the wheels. Processing unit  110  may use this information to determine a road surface profile at the location of vehicle  200 . The road surface profile may provide information regarding a bank or inclination of, for example, lane  5902  (or  5602 ) relative to road center  5906  or right side  5908 . In some embodiments, the road surface profile may also identify a bump in road segment  5900  (or  5600 ) based on the signals from the one or more suspension sensors  5500 . 
     Process  6000  may also include a step  6004  of determining a local feature based on the information received from the sensor (e.g. imaging unit  110 , one or more suspension sensors  5500 , etc.). The local feature may represent one or more aspects of the road segment at current location  5628  or  5932  of vehicle  200 . For example, the local feature may include at least one of a road width, a lane width, or a road surface profile at current location  5932  of vehicle  200 . In some exemplary embodiments, the local feature may be based on data collected by processing unit  110  as vehicle travels along predetermined road model trajectory to current location  5932 . In particular, based on road widths, lane widths, lengths of road markings (dashes), spacing between adjacent road markings (dashes), etc., determined as vehicle travels to current location  5932 , processing unit  110  may determine a road width profile, a lane width profile, a dashed line length profile, a dashed line spacing profile, second parameter values representing a curvature of traveled trajectory  5914 , and/or other parameters as discussed above with respect to  FIGS. 55-58 . 
     Process  6000  may include a step  6006  of receiving predetermined signature features for road segment  5900 . For example, processing unit  110  may retrieve the predetermined signature features from a database  160  stored in memories  140 ,  150  associated with vehicle  200  or from a database  160  located remotely from vehicle  200 . As discussed above with respect to  FIGS. 56-59 , the predetermined signature features may include one or more predetermined parameter values representing at least one of a road width, a lane width, a dashed line length, a dashed line spacing, etc., at predetermined locations along predetermined road model trajectory  5912 . In some exemplary embodiments, the predetermined signature features may also include one or more of a road width profile over at least a portion of the road segment, a lane width profile over at least a portion of the road segment, a dashed line spacing profile over at least a portion of the road segment, a predetermined number of road markings along at least a portion of the road segment, a road surface profile over at least a portion of the road segment, or a predetermined curvature associated with the road segment at various predetermined locations along predetermined road model trajectory  5912 . In some exemplary embodiments, processing unit  110  may retrieve a first set of parameter values representing at least one predetermined signature feature of road segment  5900 . 
     Further still, in some exemplary embodiments, the predetermined signature features may start at a known location (e.g., an intersection) and, if lane marking segment lengths and spacing are known and lane marking segments are counted, processing unit  110  may determine a location about the road from the known location. In some embodiments, a combination of known lengths for specific segments (e.g., typically close to the intersection) together with statistics on regarding consistent segment lengths and spacing may also be used as a predetermined signature feature. Further, in some embodiments, a predetermined signature feature may include a combination of two repetitive features, such as the combination of lane marking segments and lampposts. In still yet other embodiments, a predetermined signature feature may include a combination of GPS data (e.g., an approximate location) and lane mark segments. 
     Process  6000  may also include a step  6008  of determining whether the local feature determined, for example, in step  6004  matches the at least one predetermined signature feature, retrieved for example in step  6006 . Processing unit  110  may determine whether there is a match as discussed above with respect to  FIGS. 57-59 . When processing unit  110  determines that the local feature matches a predetermined signature feature (Step  6008 : Yes), processing unit  110  may proceed to step  6010 . In step  6010 , processing unit may determine current location  5628 ,  5932  of vehicle  200 . Processing unit  110  may determine current location  5932  as discussed above with respect to  FIGS. 57-59 . Returning to step  6008 , when processing unit  110  determines, however, that the local feature does not match a predetermined signature feature (Step  6008 : No), processing unit  110  may return to step  6006  to retrieve another predetermined signature feature from database  160 . 
     Process  6000  may include a step  6012  of determining heading direction  5980  of vehicle  200  at current location  5628 ,  5932 . Processing unit  110  may determine heading direction  5980  using one or more operations discussed above with respect to  FIGS. 37-39 . For example, processing unit  110  may determine heading direction  5980  as a gradient of traveled trajectory  5914  at current location  5932  of vehicle  200 . Process  6000  may also include a step  6014  of determining a direction  5990  of predetermine road model trajectory  5912 . Processing unit  110  may determine direction  5990  using one or more operations discussed above with respect to  FIGS. 37-43 . For example, processing unit  110  may determine direction  5990  as a vector oriented tangentially to predetermined road model trajectory  5912  at current location  5932  of vehicle  200 . Processing unit  110  may determine the tangential vector as a vector pointing along a gradient of the mathematical representation of predetermined road model trajectory  5912  at current location  5932 . 
     Process  6000  may also include a step  6016  of determining an autonomous steering action for vehicle  200 . Processing unit  110  may determine a rotational angle  0  between heading direction  5980  and direction  5990  of predetermined road model trajectory  5912 . Processing unit  110  may execute the instructions in navigational module  408  to determine an autonomous steering action for vehicle  200  that may help ensure that heading direction  5980  of vehicle  200  is aligned (i.e., parallel) with direction  5990  of predetermined road model trajectory  5912  at current location  5932  of vehicle  200 . Processing unit  110  may also send control signals to steering system  240  to adjust rotation of the wheels of vehicle  200  to turn vehicle  200  so that heading direction  5980  may be aligned with direction  5990  of predetermined road model trajectory  5912  at current location  5932 . 
     Processing unit  110  and/or image acquisition unit  120  may repeat steps  6002  through  6016  after a predetermined amount of time. In one exemplary embodiment, the predetermined amount of time may range between about 0.5 seconds to 1.5 seconds. By repeatedly determining current location  5932  of vehicle  200  based on road signatures, heading direction  5980  based on traveled trajectory  5914 , direction  5990  of predetermined road model trajectory  3410  at current location  5932 , and the autonomous steering action required to align heading direction  5980  with direction  5990 , processing unit  110  and/or image acquisition unit  120  may help to navigate vehicle  200 , using road signatures, so that vehicle  200  may travel along road segment  5912 . 
     Forward Navigation Based on Rearward Facing Camera 
     Consistent with disclosed embodiments, in situations where adverse lighting conditions inhibit navigation using a forward facing camera (e.g., driving into bright sun), navigation can be based on image information obtained from a rearward facing camera. 
     In one embodiment, a system for autonomously navigating a vehicle may include at least one processor. The at least one processor may be programmed to receive from a rearward facing camera, at least one image representing an area at a rear of the vehicle, analyze the at least one rearward facing image to locate in the image a representation of at least one landmark, determine at least one indicator of position of the landmark relative to the vehicle, determine a forward trajectory for the vehicle based, at least in part, upon the indicator of position of the landmark relative to the vehicle, and cause the vehicle to navigate along the determined forward trajectory. 
     In related embodiments, the indicator of position of the landmark may include a distance between the vehicle and the landmark and/or a relative angle between the vehicle and the landmark. The landmark may include a road marking, a lane marking, a reflector, a pole, a change in line pattern on a road, a road sign, or any other observable feature associated with a road segment. The landmark may include a backside of a road sign, for example. The at least one processor may be further programmed to determine a lane offset amount of the vehicle within a current lane of travel based on the indicator of position of the landmark, and determination of the forward trajectory may be based on the determined lane offset amount. The at least one processor may be further programmed to receive from another camera, at least one image representing another area of the vehicle, and determination of the forward trajectory may be further based on the at least one image received from the other camera. 
     In some embodiments, the rearward facing camera may be mounted on an object connected to the vehicle. The object may be a trailer, a bike carrier, a mounting base, a ski/snowboard carrier, or a luggage carrier. The rearward camera interface may be a detachable interface or a wireless interface. 
       FIG. 61A  is a diagrammatic side view representation of an exemplary vehicle  6100  consistent with the disclosed embodiments. Vehicle  6100  may be similar to vehicle  200  of  FIG. 2A , except that vehicle  6100  includes in its body an image capture device  6102  facing in a rearward direction relative to vehicle  6100 . System  6104  may be similar to system  100  of  FIG. 1  and may include a processing unit  6106  similar to processing unit  110 . As shown in  FIG. 61A , image capture device  6102  may be positioned in the vicinity of a trunk of vehicle  6100 . Image capture device  6102  may also be located, for example, at one of the following locations: on or in a side mirror of vehicle  6100 ; on the roof of vehicle  6100 ; on a side of vehicle  6100 ; mounted on, positioned behind, or positioned in front of any of the windows/windshield of vehicle  6100 ; on or in a rear bumper; mounted in or near light figures on the back of vehicle  6100 ; or any other locations where image capture device  6102  may capture an image of an area rear of vehicle  6100 . In some embodiments, as discussed above, image capture device  6102  may be mounted behind a glare shield that is flush with the rear windshield of vehicle  6100 . Such a shield may minimize the impact of reflections from inside vehicle  6100  on image capture device  6102 . 
       FIG. 61A  shows one image capture device  6102  facing a rearward direction of vehicle  6100 . However, other embodiments may include a plurality of image capture devices located at different positions and facing a rearward direction of vehicle  6100 . For example, a first image capture device may be located in the trunk of vehicle  6100  facing a rearward, slightly downward direction of the vehicle  6100 , and a second image capture device may be mounted on the roof of vehicle  6100  facing a rearward, slightly upward direction of vehicle  6100 . In another example, a first image capture device may be mounted on a left side mirror of vehicle  6100 , and a second image capture device may be mounted on a right side mirror of vehicle  6100 . Both the first and second image capture devices may face a rearward direction of vehicle  6100 . 
     In some embodiments, the relative positioning of the image capture devices may be selected such that the fields of view of the image capture devices overlap fully, partially, or not at all. Further the image capture devices may have the same or different fields of view and the same or different focal lengths. 
       FIG. 61A  shows an image capture device  6102  facing in a rearward direction relative to vehicle  6100 . However, a skilled artesian would recognize that vehicle  6100  may further include any number of image capture devices facing in various directions and that processing unit  6106  may be further programmed to operate these additional image capture devices. For example, vehicle  6100  may further include image capture devices  122  and  124  of  FIG. 2A , and processing unit  6106  may be programmed to perform the programmed functions of processing unit  110  of system  100 . A skilled artesian would further recognize that having a plurality of image capture devices, one facing a rearward direction of vehicle  6100  and another facing a forward direction of vehicle  6100 , may be beneficial in situations where adverse lighting conditions may inhibit navigation using one of the image capture devices (e.g., driving into bright sun). Since adverse lighting conditions rarely affect both image capture devices thereof, system  6104  may be configured to navigate based on images received from an image capture device affected less adversely by the lighting condition in these situations. 
     In some embodiments, a forward trajectory for the vehicle may be determined based on an image received from a rearward facing camera together with or independent from an image received from a forward facing camera. In some embodiments, a forward trajectory may be determined by, for example, averaging two determined trajectories, one based on images received from a rearward facing camera, and another based on images received from a forward facing camera. 
     In some embodiments, images from a forward facing camera and a rearward facing camera may be analyzed to determine which is currently providing more useful images. Based on this determination, images from the forward facing camera or the rearward facing camera may selectively be used in navigating the vehicle. For example, in a situation where vehicle  6100  may face a bright light source (e.g., the sun) that causes the forward facing camera to capture an image lacking sufficient detail on which navigational responses may accurately be determined, images collected from a rearward facing camera, not affected by the same light source may be used in navigating the vehicle. This determination and selection of images from the available image streams may be made on the fly. 
     In some embodiments, navigation may be based on images received from a rearward facing camera because one or more objects (e.g., a large truck or other vehicle) is blocking a portion of a field of view of a forward facing camera. In other situations, navigation may be based on a images collected from a rearward facing camera as a supplement to images collected from a forward facing camera. For example, in some embodiments a vehicle may locate a recognized landmark in a field of view of its forward facing camera. From a time when that recognized landmark first comes into view of the forward facing camera until a time when the vehicle has passed the recognized landmark (or the landmark has otherwise passed out of the field of view of the forward facing camera), navigation can proceed based on images captured of the recognized landmark (e.g., based on any of the techniques described above). Navigation based on the recognized landmark, however, need not end when the vehicle passes the landmark. Rather, a rearward facing camera can capture images of the same recognized landmark as the vehicle travels away from the landmark. These images can be used, as described above, to determine a location of the vehicle relative to a target trajectory for a particular road segment, and images of the backside of the recognized landmark may be usable as long as the backside of the landmark is visible or appears in the images captured by the rearward facing camera. Using such a technique may extend an amount of time that a vehicle can navigate with the benefit of a recognized landmark and delay a time when the vehicle must transition to dead reckoning or another navigational technique not anchored by a known location of a recognized landmark. As a result, navigational error may be even further reduced such that the vehicle even more closely follows a target trajectory. 
     In some embodiments, an object(s) may be present at the back of vehicle  6100 , and this object may be in the field of vision of image capture device  6102 , interfering with image capture device&#39;s  6102  ability to accurately capture images representing an area at a rear of vehicle  6100 . The object may be, for example, a trailer, a mounting base, a bike carrier, a ski/snowboard carrier, or luggage carrier. In these embodiments, image capture device  6102  may be mounted on the object and arranged to capture images representing an area at a rear of the object.  FIG. 61B  illustrates an exemplary vehicle with such an object. 
       FIG. 61B  is a diagrammatic side view representation of an exemplary vehicle  6150  consistent with the disclosed embodiments. Vehicle  6150  is similar to vehicle  6100  except that vehicle  6150  is towing a trailer  6108  and image capture device  6102  is mounted on trailer  6108 . As shown in  FIG. 61B , image capture device  6102  is facing a rearward direction of trailer  6108  and positioned to capture images representing an area at a rear of trailer  6108 . As discussed above, the presence of trailer  6108  may interfere with any image capture devices that may be mounted on the body of vehicle  6150  and face a rearward direction of vehicle  6108 . 
     In some embodiments, image capture device  6102  of  FIG. 61B  may have been previously mounted in or on the body of vehicle  6150  (similar to image capture device  6102  of  FIG. 61A ) and is now repositioned on trailer  6108 . In these embodiments, image capture device  6102  may be electrically connected to system  6104  via a detachable electrical interface  6110 . A “detachable electrical interface” may be broadly defined as a set of connectors that can be connected and disconnected by a driver or a passenger (or any other person, who may not be a skilled artesian). Detachable electrical interface  6110  allows a user who may not be a skilled artesian, such as the driver or passengers of vehicle  6150 , to remount image capture device  6102 , for example, from trailer  6108  to vehicle  6150 . This capability may be especially useful in situations where vehicle  6150  frequently switches between operating with and without trailer  6108 . In other embodiments, rearward facing camera  6102  may be configured to communicate wirelessly with processing unit  6106 . 
       FIG. 62  is a diagrammatic top view representation of an exemplary vehicle autonomously navigating on a road consistent with disclosed embodiments.  FIG. 62  shows vehicle  6100  of  FIG. 61A  including image capture device  6102  (with its line of sight  6102 A) and system  6104  for autonomously navigating vehicle  6100 .  FIG. 62  also shows several potential, recognized landmarks, including a road roughness profile  6208  associated with a particular road segment, a change in lane markings  6210 , reflectors  6202 A-C, a road sign  6204  facing away from vehicle  6100 , a road sign  6206  facing towards vehicle  6100 , and a pole  6214 . 
       FIG. 62  also shows indicators of position of a landmark relative to vehicle  6100 . The indicators of position in  FIG. 62  include a distance  6212 A and/or a relative angle  6212 B between a landmark (e.g., reflector  6202 A) and vehicle  6100 . An “indicator of position” may refer to any information that relates to a position. Thus, an indicator of position of a landmark may include any information related to the position of the landmark. In the example of  FIG. 62 , indicators of position of a landmark are determined relative to vehicle  6100 . 
     As shown in  FIG. 62 , distance  6212 A is the distance between image capture device  6102  and the landmark, and angle  6212 B is the angle between line of sight  6102 A of image capture device  6102  and an imaginary line from image capture device  6102  to the landmark. However, in some embodiments, distance  6212 A may be the distance between a reference point in the vicinity of vehicle  6100  and the landmark, and angle  6212 B may be the angle between a reference line through the reference point and an imaginary line from the reference point to the landmark. The reference point may be, for example, the center of vehicle  6100 , and the reference line may be, for example, a line through the center of vehicle  6100 . 
     In some embodiments, one or more landmarks and/or one or more indicators of position may be used in autonomous navigation of vehicle  6100 . For example, the indicators of position may be used to determine a current location of a vehicle relative to a target trajectory stored in sparse map  800 , for example. Any of the techniques discussed above with respect to landmark recognition and use in determining one or more navigational responses for a vehicle may be employed based on images received from a rearward facing camera. 
       FIG. 63  is a flowchart showing an exemplary process for using one or more landmarks and one or more indicators of position for autonomously navigating a vehicle. Process  6300  may use at least one image from a rearward facing camera and analyze the at least one image to navigate a vehicle along a forward trajectory. 
     At step  6302 , processing unit  6106  may receive from a rearward facing camera, at least one image representing an area at a rear of vehicle  6100 . At an optional step, processing unit  6106  may receive from another camera, at least one image representing another area of vehicle  6100 . In some embodiments, processing unit  6106  may receive the images via one or more camera interfaces. For example, processing unit  6106  may receive at least one image representing an area at a rear of the vehicle via a rearward camera interface and receive one image representing an area at a front of the vehicle via a forward camera interface. In some embodiments, as discussed above, the one or more camera interfaces may include a detachable interface or a wireless interface. 
     At step  6304 , processing unit  6106  may analyze the at least one rearward facing image to locate in the image a representation of at least one landmark. As discussed above in reference to  FIG. 62 , a landmark may include, for example, a road profile  6208 , a change in lane markings  6210 , reflectors  6202 A-C, a road sign  6204  facing away from vehicle  6100 , a road sign  6206  facing towards vehicle  6100 , and a pole  621 . Alternatively, or additionally, a landmark may include, for example, a traffic sign, an arrow marking, a lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, a lamppost, a directional sign, a speed limit sign, a road marking, a business sign, a distance marker, or a change in spacing of lines on the road. 
     In some embodiments, before or after a landmark is located in the received image, processing unit  6106  may retrieve information relating to recognized landmarks in the vicinity of the autonomous vehicle. The information relating to landmarks may include, for example, information relating to size and/or shape of a landmark. The information relating to landmarks may be retrieved from, for example, a database, which may be located in system  6104  or located external to vehicle  6100  (connected to system  6104  via a communication system such as a cellular network or other wireless platform). 
     At step  6306 , processing unit  6106  may determine at least one indicator of position of the landmark relative to the vehicle. For example, an indicator of position may include a distance between a landmark and the vehicle and/or a relative angle between a landmark and the vehicle. As discussed above in reference to  FIG. 62 , the distance may be, for example, distance  6212 A, which is the distance between image capture device  6102  and the landmark, and the angle may be angle  6212 B, which is the angle between line of sight  6102 A of image capture device  6102  and an imaginary line from image capture device  6102  to the landmark. 
     In some embodiments, processing unit  6106  may determine at least one indicator of position of the landmark relative to the vehicle based on the representation of the located landmarks in the received image. For example, the size and shape of the representation of the located landmarks in the received image may be used to estimate distance  6212 A from vehicle  6100  (e.g., by monitoring a change in scale of the object over multiple image frames). In another example, coordinates of the pixels occupied by the representation of the located landmarks in the received image may be used to estimate angle  6212 B. In embodiments where information relating to landmarks is retrieved by processing unit  6106 , the information may be used to model and compare with the representation of the located landmarks in the received image. In these embodiments, the information may improve the accuracy of the determined indicators of position. 
     At an optional step  6308 , processing unit  6106  may determine a lane offset amount of the vehicle within a current lane of travel (or even make a determination of a current lane of travel) based on the indicator of position of the landmark relative to the vehicle. For example, such an offset determination or lane determination may be determined by knowing a position of a recognized landmark along with a relative positioning of the recognized landmark with respect to lanes of a road segment. Thus, once a distance and direction are determined relative to a recognized landmark, the current lane of travel and/or amount of lane offset within a particular lane of travel may be calculated. A lane offset of a vehicle may refer to a perpendicular distance from a lane indicator to a reference point. In some embodiments, the reference point may correspond to an edge of a vehicle or a point along a centerline of a vehicle. In other embodiments, the reference point may correspond to a mid-point of a lane or a road. A lane indicator may include, for example, a lane marking, a road edge, reflectors for improving visibility of a lane, or any other object that is on or near the boundaries of a lane. In the above example, the lane offset may be the perpendicular distance from road edge  6208  to vehicle  6100 . 
     At step  6310 , processing unit  6106  may determine a forward trajectory for the vehicle based, at least in part, upon the indicator of position of the landmark relative to the vehicle. In embodiments where the optional step  6308  is performed, processing unit  6106  may determine the forward trajectory for the vehicle further based on the determined lane offset amount. In embodiments where processing unit  6106  received from another camera, at least one image representing another area of vehicle  6100 , processing unit  6106  may determine the forward trajectory for the vehicle further based on the at least one image received from the another camera. Such a trajectory determination may be based on any of the techniques described above (e.g., navigation based on recognized landmarks, tail alignment, etc.) 
     At step  6312 , processing unit  6106  may cause vehicle  6100  to navigate along the determined forward trajectory. In some embodiments, processing unit  6106  may cause one or more navigational responses in vehicle  6100  to navigate along the determined forward trajectory. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof to navigate along the determined forward trajectory. For instance, processing unit  6106  may cause vehicle  6100  to shift one lane over and then accelerate by, for example, sequentially transmitting control signals to steering system  240  and throttling system  220 . Alternatively, processing unit  110  may cause vehicle  6100  to brake while at the same time shifting lanes by, for example, simultaneously transmitting control signals to braking system  230  and steering system  240 . 
     Navigation Based on Free Space Determination 
     Consistent with disclosed embodiments, the system can recognize parked vehicles, road edges, barriers, pedestrians, and other objects to determine free space boundaries within which the vehicle can travel. 
     In some situations, free space boundaries may be used to navigate a vehicle. These situations may include, for example, when lane markings are not visible because lanes do not exist and/or because obstacles are covering the lane marks (e.g., parked cars and snow). Alternatively, free space boundaries may be used to navigate a vehicle in addition to the lane-mark-based navigation method to increase the robustness of the system. 
       FIG. 64  is a diagrammatic perspective view of an environment  6400  captured by a forward facing image capture device on an exemplary vehicle consistent with disclosed embodiments. The exemplary vehicle may be, for example, vehicle  200  described above in reference to  FIGS. 2A-2F  and may include a processing unit, such as processing unit  110  of vehicle  200 . The forward facing image capture device may be, for example, image capture device  122 , image capture device  124 , or image capture device  126  of vehicle  200 . 
       FIG. 64  shows a non-road area  6410  with a road edge  6410 A, a sidewalk  6412  with a curb  6412 A, and a road horizon  6414 . A road barrier  6418  may be present in the vicinity of road edge  6410 A, and a car  6416  may be parked in the vicinity of curb  6412 A.  FIG. 64  also shows a first free space boundary  6404 , a second free space boundary  6406 , and a forward free space boundary  6408 . Forward free space boundary  6408  may extend between first free space boundary  6404  and second free space boundary  6406 . A free space region  6402  forward of the vehicle (not shown in the figure) may be a region bound by these three boundaries and may represent a physically drivable region within environment  6400 . First and second free space boundaries  6404 ,  6406  may each correspond to, for example, a road edge, a road barrier, a parked car, a curb, a lane dividing structure, a tunnel wall, and/or a bridge structure, or a combination thereof. Forward free space  6408  may correspond to, for example, an end of the road, a road horizon, a road barrier, a vehicle, or a combination thereof. 
     In the example of  FIG. 64 , first and second free space boundaries  6404 ,  6406  may each correspond to a plurality of objects. For example, first free space boundary  6404  may correspond to a portion of road edge  6410 A and road barrier  6418 , and second free space boundary  6406  may correspond to a portion of curb  6412 A and parked car  6414 . However, in some embodiments, each free space boundary may correspond to a single object. Similarly, forward free space  6408  may correspond to one or more objects. For example, in  FIG. 64 , forward free space  6408  corresponds to road horizon  6414 . 
     In some embodiments, one or more obstacles may exist in free space region  6402  bound by the three free space boundaries  6404 ,  6406 , and  6408 . In these embodiments, the obstacles may be excluded from free space region  6402 . In  FIG. 64 , for example, pedestrian  6420  is standing inside free space region  6402  bound by the three free space boundaries  6404 ,  6406 , and  6408 . Therefore, pedestrian  6420  may be excluded from free space region  6402 . Alternatively, regions surrounding the obstacles may be excluded from free space region  6402 . For example, a region  6422  surrounding pedestrian  6420 , instead of the region occupied by pedestrian  6420 , may be excluded from free space region  6402 . Obstacles may include, for example, a pedestrian, another vehicle, and debris. 
     A size of a region (e.g., region  6422 ) surrounding an obstacle (e.g., pedestrian  6420 ) may determine the minimum distance that may exist between the vehicle and the obstacle during navigation. In some embodiments, the size of the region may be substantially the same as the size of the obstacle. In other embodiments, the size of the region may be determined based on the type of the obstacle. For example, region  6422  surrounding pedestrian  6420  may be relatively large for safety reasons, while another region surrounding debris may be relatively small. In some embodiments, the size of the region may be determined based on the speed at which the obstacle is moving, frame rate of the image capture device, speed of the vehicle, or a combination thereof. In some embodiments, a shape of a region surrounding an obstacle may be a circle, a triangle, a rectangle, or a polygon. 
     In  FIG. 64 , first free space boundary  6404  corresponds to a portion of road edge  6410  and road barrier  6148 , and second free space boundary  6406  corresponds to a portion of curb  6412 A and parked car  6416 . However, in other embodiments, first and second free space boundaries  6404 ,  6406  may correspond to road edge  6410 A and curb  6412 A, respectively, and road barrier  6148  and parked car  6416  may be considered as obstacles. 
     In some embodiments, regions between obstacles may be excluded from free space region  6402 . For example, if a width of a region between two obstacles is less than the width of the vehicle, the region may be excluded from free space region  6402 . 
       FIG. 65  is an exemplary image received from a forward facing image capture device of a vehicle consistent with disclosed embodiments.  FIG. 65  shows a first free space boundary  6504  and a second free space boundary  6506 . Both free space boundaries correspond to curbs and parked cars (e.g., parked car  6516 ) on each side of the road.  FIG. 65  also shows a free space region  6502 , which may be defined by first free space boundary  6504 , a second free space boundary  6506 , and a forward free space boundary (not shown). Additionally,  FIG. 65  shows a moving car  6520 , which may be considered as an obstacle. Therefore, moving car  6520  may be excluded from free space region  6502 . 
       FIG. 66  is a flowchart showing exemplary process  6600  for navigating vehicle  200  based on free space region  6402  in which vehicle  200  can travel consistent with disclosed embodiments. Process  6300  may use a plurality of images from a forward facing image capture device, analyze at least one image of the plurality of images to identify free space boundaries and define a free space region bound by the identified free space boundaries. Furthermore, process  6300  may navigate a vehicle based on the defined free space region. 
     At step  6602 , processing unit  110  may receive from image capture device  122 , a plurality of images associated with environment  6400  of vehicle  200 . As discussed above,  FIG. 65  is an example of an image that may be received from image capture device  122 . In some embodiments, images may be captured at different times by image capture device  122  (e.g., images may be captured apart by less than a second, 1 second, 2 second, etc.). In some embodiments, vehicle  200  may include a plurality of image capture devices (e.g., image capture devices  122  and  124  of vehicle  200 ), and processing unit  110  may receive from each image capture device, a plurality of images associated with environment  6400  of vehicle  200 . The plurality of images received from each image capture device may be images captured at different times by each image capture device. 
     At step  6604 , processing unit  110  may analyze at least one of the plurality of images received from, for example, image capture device  122 . In embodiments where a single plurality of images is generated based on images received from a plurality of image capture devices, processing unit  110  may analyze at least one image of the single plurality of images. Alternatively, each image received from each image capture device may be analyzed independently. 
     Additionally, processing unit  110  may identify a first free space boundary  6404  on a driver side of vehicle  200  and extending forward of vehicle  200 , a second free space boundary  6406  on a passenger side of vehicle  200  and extending forward of vehicle  200 , and a forward free space boundary  6408  forward of vehicle  200  and extending between first free space boundary  6404  and second free space boundary  6406 . Additionally, processing unit  110  may further identify a free space region  6402  forward of the vehicle as the region bound by first free space boundary  6404 , the second free space boundary  6406 , and forward free space boundary  6408 . As discussed above, first and second free space boundaries  6404 ,  6406  may each correspond to, for example, a road edge, a road barrier, a parked car, a curb, a lane dividing structure, a tunnel wall, and/or a bridge structure, or a combination thereof. Furthermore, as discussed above, forward free space  6408  may correspond to, for example, an end of the road, a road horizon, a road barrier, a vehicle, or a combination thereof. 
     At an optional step, processing unit  110  may identify, based on the analysis at step  6604 , an obstacle (e.g., pedestrian  6420 ) forward of vehicle  200  and exclude the identified obstacle from free space region  6402  forward of vehicle  200 . Alternatively, processing unit  110  may identify, based on analysis of the at least one of the plurality of images at step  6640 , an obstacle (e.g., pedestrian  6420 ) forward of the vehicle and exclude a region (e.g., region  6422 ) surrounding the identified obstacle from free space region  6402  forward of the vehicle. 
     In some embodiments, as discussed above, the size of the region surrounding the identified obstacle may be substantially the same as the size of the obstacle, or alternatively, the size of the region surrounding the obstacle may be determined based on the type of obstacle. In some embodiments, as discussed above, the size of the region may be determined based on the speed at which the obstacle is moving, a frame rate of image capture device  122 , speed of vehicle  200 , or a combination thereof. 
     At another optional step, processing unit  110  may exclude, from free space region  6402 , regions between the identified obstacles and/or regions between identified obstacles and free space boundaries  6404 ,  6406 . In some embodiments, as discussed above, processing unit  110  may determine whether to exclude the regions between the identified obstacles based on a distance between the identified obstacles. Furthermore, processing unit  110  may determine whether to exclude the regions between identified obstacles and free space boundaries  6404 ,  6406  based on the distance between identified obstacles and free space boundaries  6404 ,  6406 . 
     At step  6606 , processing unit  110  may determine a navigational path for vehicle  200  through free space region  6402 . In some embodiments, the navigational path may be a path through the center of free space region  6402 . In other embodiments, the navigational path may be a path that is a predetermined distance away from one of first and second free space boundaries  6404 ,  6406 . The predetermined distance may be a fixed distance, or alternatively, the predetermined distance may be determined based on, for example, one or more of the following: speed of vehicle  200 , width of free space region  6402 , and number of obstacles within free space region  6402 . Alternatively, the navigational path may be, for example, a path that uses the minimum number of navigational responses or the shorted path. 
     At step  6608 , processing unit  110  may cause vehicle  200  to travel on at least a portion of the determined navigational path within the free space region  6402  forward of vehicle  200 . In some embodiments, processing unit  110  may cause one or more navigational responses in vehicle  200  to navigate along the determined navigational path. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof to navigate along the determined forward trajectory. For instance, processing unit  110  may cause vehicle  200  to move laterally and then accelerate by, for example, sequentially transmitting control signals to steering system  240  and throttling system  220 . Alternatively, processing unit  110  may cause vehicle  200  to brake while at the same time moving laterally, for example, simultaneously transmitting control signals to braking system  230  and steering system  240 . Further, for example, the free space boundary may serve as a localization aid in the same way that lane marks are being used. Once the free space boundary is encoded, it may describe a 3D curve in space. At the localization stage, the projection of that 3D curve to the image may provide a localization cue, since it may collide with the free space detection at that location. 
     Navigating in Snow 
     Consistent with disclosed embodiments, the system may determine the edges of a road in poor weather conditions, such as when a road is covered in snow. For example, the system may take into account changes in light, the curve of a tree line, and tire tracks to determine probable locations of the edges of the road. 
       FIG. 67  is a diagrammatic top view representation of an exemplary vehicle navigating on a road with snow covering at least some lane markings and road edges consistent with disclosed embodiments. The exemplary vehicle may be, for example, vehicle  200  described above in reference to  FIGS. 2A-2F  and may include a processing unit, such as processing unit  110  of vehicle  200 . The forward facing image capture device may be, for example, image capture device  122 , image capture device  124 , or image capture device  126  of vehicle  200 . 
     In  FIG. 67 , the road may include a driver side lane mark  6702  and a passenger side lane mark  6704 .  FIG. 67  also shows a non-road area  6710  and a road edge  6706 . In one example, non-road area  6710  may be a non-paved area, a sidewalk, or a beginning of a hill. In another example, non-road area  6710  may be an area without a platform, such as an area with a sharp vertical drop (i.e., cliff). 
       FIG. 67  also shows an area covered by snow  6708 . Specifically, area  6708  covers a portion of road edge  6706  and portions of lane marks  6702 ,  6704 . Thus, road edge  6706  and/or one or more of lane marks  6702 ,  6704  may not be readily apparent through analysis of images captured during navigation of vehicle  200 . In such situations, vehicle  200  may navigate based on analysis of captured images by determining probable locations for road edges bounding the portion of the road that is covered with snow. 
     In some embodiments, the determination of the probable locations for road edges may be based on tire tracks  6712  over an area covered by snow  6708 . For example, the presence of tire tracks  6712  may indicate that the portion of an area covered by snow  6708  with tire tracks  6712  is within the bounds of road edges. In some embodiments, the processing unit of the vehicle may consider the path of the tire tracks as a viable navigational path and may cause the vehicle to follow the tire tracks subject to consideration of other criteria (e.g., whether the tracks remain within an area determined as likely corresponding to the road or, more specifically, a lane of travel for the vehicle). 
     In other embodiments, the determination of the probable locations for road edges may be based on a change of light across a surface of area covered by snow  6708 . The source of the light may include, for example, headlights of vehicle  200 , light from other vehicles, street lights, or the sun. The change of light across the surface of area covered by snow  6708  may occur for various reasons. In one example, surface roughness of non-road area  6710  and surface roughness of the road may be different; non-road area  6710  may be a gravel area, while the road may be a paved area. In another example, non-road area  6710  and the road may not be level. Non-road area  6710  may be a sidewalk, which is typically raised above the road; alternatively, non-road area  6710  may be a hill or a cliff. Each of these may alter the surface of a covering of snow and may be recognized based on certain variations in the surface of the snow covering (e.g., changes in height, changes in texture, etc.) which may be accentuated by shadows cast across the snow surface. 
     In other embodiments, the determination of the probable locations for road edges may be based on a plurality of trees (e.g., forming a tree line) along an edge of the road. For example, in  FIG. 67 , trees  6714  may be present along the road edge and visible even when snow covers road edge  6706 . In situations where trees  6714  are present close to road edge  6714 , the location of trees  6714  may be used as a probable location of a road edge. However, in some situations, trees  6714  may be present some distance away from a road edge. Therefore, in some embodiments, the probable location of a road edge may be determined as a location that is a distance away from the location of trees  6714 . The distance may be a fixed value, or alternatively, the distance may be dynamically determined based on, for example, a last visible road edge. 
     In other embodiments, the determination of the probable locations for road edges may be based on an observed changes in curvature at a surface of the snow. The change in curvature at a surface of the snow may occur for various reasons. For example, a change in curvature at a surface of snow may occur when non-road area  6710  and the road are not level. In situations where non-road area  6710  is, for example, a sidewalk typically raised above the road, the snow may pile up near road edge  6706  thereby changing the curvature of snow near road edge  6706 . In other situations, the non-road area may be a beginning of a hill, and the curvature at the surface of the snow may follow the curvature of the hill beginning at road edge  6706 . In these embodiments, the location where the curvature begins to change may be determined as a probable location of a road edge. 
       FIG. 68  is a flowchart showing an exemplary process  6800  for navigating vehicle  200  on a road with snow covering at least some lane markings and road edges consistent with disclosed embodiments. Process  6800  may use the probable locations for road edges, as described above, to navigate vehicle  200 . 
     At  6802 , processing unit  110  may receive from an image capture device, at least one environmental image forward of the vehicle, including areas where snow covers at least some lane markings (e.g., lane marks  6702 ,  6704 ) and road edges (e.g., road edge  6706 ). 
     At step  6804 , processing unit  110  may identify, based on an analysis of the at least one image, at least a portion of the road that is covered with snow and probable locations for road edges bounding the at least a portion of the road that is covered with snow. As discussed above, the analysis of the at least one image may include identifying at least one tire track in the snow, a change of light across a surface of the snow, and/or a trees along an edge of the road. Further, as discussed above, the analysis of the at least one image may include recognizing a change in curvature at a surface of the snow, where the recognized change in curvature is determined to correspond to a probable location of a road edge. 
     In some embodiments, the analysis of the at least one image may include a pixel analysis of the at least one image in which at least a first pixel is compared to at least a second pixel in order to determine a feature associated with a surface of the snow covering at least some lane markings and road edges. For example, each pixel in the image may be compared with every adjacent pixel. In some embodiments, a color of the first pixel may be compared to a color of at least the second pixel. Alternatively, or additionally, an intensity of a color component of the first pixel may be compared to an intensity of the color component of at least the second pixel. In other embodiments, the following properties of a pixel may be compared. 
     In some embodiments, the pixel analysis may identify features such as an edge of tire track  6712  or road edge  6706 . The analysis for identifying such features may include identifying a set of pixels where a rate in which a pixel property changes exceeds a threshold rate. The pixel property may include, for example, color of a pixel and/or intensity of a color component of a pixel. 
     At step  6806 , processing unit  110  may cause the vehicle to navigate a navigational path that includes the identified portion of the road and falls within the determined probable locations for the road edges. 
     In embodiments where the probable location for road edges are determined based on the identified tire tracks  6712 , processing unit  110  may cause vehicle  200  to navigate by at least partially following the identified tire tracks  6712  in the snow. In embodiments where the probable location for road edges (e.g., road edges  6702 ,  6704 ) are determined based on a change of light across a surface of area covered by snow  6708 , a plurality of trees (e.g., forming a tree line) along an edge of the road, and/or a change in curvature at a surface of the snow, processing unit  110  may cause vehicle  200  to navigate between the determined edges of the road. 
     Furthermore, in embodiments where edges of the road are determined by analyzing pixels of the image received at step  6802 , processing unit  110  may cause vehicle  200  to navigate between the determined edges of the road. In embodiments where an edge of a tire track is determined by analyzing pixels of the image received at step  6802 , processing unit  110  may cause vehicle  200  to navigate by at least partially following tire tracks in the snow. 
     In some embodiments, processing unit  110  may cause one or more navigational responses in vehicle  200  to navigate along the determined navigational path. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof to navigate along the determined forward trajectory. For instance, processing unit  110  may cause vehicle  200  to move laterally and then accelerate by, for example, sequentially transmitting control signals to steering system  240  and throttling system  220 . Alternatively, processing unit  110  may cause vehicle  200  to brake while at the same time moving laterally, for example, simultaneously transmitting control signals to braking system  230  and steering system  240 . 
     Additional techniques may also be employed by processing unit  110  for navigating a vehicle on a road at least partially covered with snow. For example, in some embodiments, one or more neural networks may be employed to aid in determination of a proposed path of travel along a road covered in snow. This technique may be referred to as holistic path prediction (HPP). Such a neural network may be trained, for example, by being supplied with images as a user drives along a road. To train the neural network in navigation of a snow covered road, various testing situations involving snow covered roads may be used. Using images (perhaps thousands of training images, millions of images, or more) of roads covered with snow captured as a driver navigates a vehicle along snow covered roads, the neural network will learn to develop a proposed navigational path along the snow. The process may involve setting up the neural network to periodically or continuously generate a proposed navigational path based on observed features of the snow covered road (including, for example, aspects of the surface of the road, edges of the road, sides of the road, barriers present, objects adjacent to the road, cars on the road, etc.) and test the proposed navigational path against actual behavior of the driver. Where the proposed navigational path diverges from the actual path the driver follows, the neural network will analyze the available images and make adjustments to its processing algorithm in order to provide a different response in a similar situation in the future (e.g., to provide a proposed navigational path that more closely matches the behavior of the driver). Once trained, the neural network may provide a proposed navigational path over a road covered with snow. Navigation through snow may be based solely on the output of a single trained neural network 
     In some embodiments, however, other techniques may be used to navigate the vehicle through snow. In some embodiments, the free space determination technique described in another section of this disclosure may be used to define a path forward of the vehicle through an area perceived as free space. For example, based on a captured image or image stream, processing unit  110  may analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle. A second free space boundary may be identified on a passenger side of the vehicle and extending forward of the vehicle. A forward free space boundary may be identified forward of the vehicle and extending between the first free space boundary and the second free space boundary. Of course, these boundaries need not be straight lines, but instead, can be represented by a complex series of curves or line segments that delineate sometimes highly irregular boundary conditions (especially on the sides of the vehicle). Together, first free space boundary, the second free space boundary, and the forward free space boundary define a free space region forward of the vehicle. Processing unit  110  may then determine a proposed navigational path for the vehicle through the free space region. Navigation of the vehicle through snow may be based on the free space determination technique alone. It should be noted that the free space determination technique may be implemented using one or more neural networks. In some embodiments, the neural network that implements the free space determination technique may be different from the neural network that implements the HPP technique. 
     In some embodiments, navigation through snow may be based on one or more techniques used in combination. For example, any of the disclosed navigational systems may be used together to navigate a vehicle in snow. In some embodiments, the free space determination technique may be combined with the HPP technique. That is, a plurality of captured images may be supplied to a neural network implementing the free space technique in order to obtain a first proposed navigational path for the vehicle. The plurality of captured images may also be supplied to the neural network implementing the HPP technique to obtain a second proposed navigational path for the vehicle. If the processing unit determines that the first proposed navigational path agrees with the second proposed navigational path, then the processing unit may cause the vehicle to travel on at least a portion of one of the proposed navigational paths (or an aggregate of the proposed navigational paths). In this context, agreement does not necessarily require an exact match of the proposed navigational paths. Rather, agreement may be determined if the proposed navigational paths have greater than a predetermined degree of correlation (which may be determined using any suitable compare function). 
     If the first proposed navigational path does not agree with the second proposed navigational path, then a prompt may be provided to the user to take over control of at least some aspect of the vehicle. Alternatively, additional information may be considered in order to determine an appropriate navigational path for the vehicle. For example, where there is disagreement in the proposed navigational paths from the free space and HPP techniques, processing unit  110  may look to a target trajectory from sparse data map  800  (along with an ego motion estimation or landmark based determination of a current position relative to the target trajectory) to determine a direction of travel for the vehicle. Outputs from other modules operating on processing unit  110  may also be consulted. For example, a vehicle detection module may provide an indication of the presence of other vehicles in the environment of the host vehicle. Such vehicles may be used to aid in path prediction for the host vehicle (e.g., by following a lead vehicle, avoiding a parked vehicle, etc.). A hazard detection module may be consulted to determine the presence of any edges in or along the roadway having a height exceeding a threshold. A curve detection module may be consulted to locate a curve forward of the vehicle and to propose a path through the curve. Any other suitable detection/analysis module operating on processing unit  110  may also be consulted for input that may aid in establishing a valid path forward for the host vehicle. 
     The description and the figures above show a road that is covered by snow; however, in some embodiments, a road may be covered with object(s) other than snow. For example, the road may be covered with sand or gravel instead of snow, and the disclosed embodiments may similarly be applied to roads covered with these objects. 
     Autonomous Vehicle Speed Calibration 
     In some situations, vehicle navigation can be based on dead reckoning (for example, at least for short segments) where the vehicle determines its current location based on its last known position, its speed history, and its motion history. Dead reckoning, however, may introduce accumulating errors because every new position determination may rely upon measurements of translational and rotational velocities, which may introduce a certain level of error. Similarly, each new position determination may rely upon a previously determined coordinate, which, in turn, may have been based on measurements including their own inaccuracies. Such inaccuracies and errors may be imparted into the dead reckoned position determinations through various sources, such as the outputs of vehicle speed sensors for example. Even small inaccuracies in speed sensing may accumulate over time. For example, in some cases, small errors in speed sensing (e.g., on the order of 1 km/hr or even less) may result position determination errors on the order of 1 meter, 5 meters, or more over a kilometer. Such errors, however, may be reduced or eliminated through calibration of vehicle speed sensors. According to the disclosed embodiments, such calibration may be performed by an autonomous vehicle based on known landmark positions or based on a reference distance along a road segment being traversed by the vehicle. 
       FIG. 69  is a diagrammatic top view representation of an exemplary vehicle including a system for calibrating a speed of the vehicle consistent with disclosed embodiments. The exemplary vehicle may be, for example, vehicle  200  described above in reference to  FIGS. 2A-2F  and may include a processing unit, such as processing unit  110  of vehicle  200 . The forward facing image capture device may include, for example, image capture device  122 , image capture device  124 , or image capture device  126  of vehicle  200 . Such image capture devices may be configured to obtain images of an environment forward, to the side, and/or to the rear of vehicle  200 . 
     In some embodiments, vehicle  200  may include various sensors. Such sensors may include one or more speed sensors, GPS receivers, accelerometers, etc. 
     In some embodiments, recognized landmarks may be used in a speed calibration process for the vehicle. Such recognized landmarks may include those landmarks represented in sparse map  800 , for example.  FIG. 69  shows examples of landmarks that may be used for calibrating speed of vehicle  200 . For example,  FIG. 69  shows landmarks such as a traffic sign  6906 , a dashed lane marking  6902 , a traffic light  6908 , a stop line  6912 , reflectors  6910 , and a lamp post  6914 . Other landmarks may include, for example, an arrow marking, a directional sign, a landmark beacon, a speed bump  6904 , etc. 
     In some embodiments, processing unit  110  of vehicle  200  may identify one or more recognized landmarks. Processing unit  110  may identify the one or more recognized visual landmarks based on any of the previously described techniques. For example, processing unit  110  may receive a local map associated with sparse map  800  (or may even receive or be loaded with sparse map  800 ) including representations of recognized landmarks. Because these landmarks may be indexed and/or because processing unit  110  may be aware of a current position of vehicle  200  (e.g., with respect to a target trajectory along a road segment), processor unit  110  may anticipate a location for the next expected recognized landmark as it traverses a road segment. In this way, processor unit  110  may even “look” to a particular location within images received from image capture device  122  where the next recognized landmark is expected to appear. Once the recognized landmark is located within a captured image or captured images, processor unit  110  may verify that the landmark appearing in the images is the expected recognized landmark. For example, various characteristics associated with the landmark in a captured image may be compared with information stored in sparse data map  800  relative to the recognized landmark. Such characteristics may include a size, landmark type (e.g., speed limit sign, hazard sign, etc.), position, distance from a previous landmark, etc. If the observed characteristics for a landmark match those stored relative to a recognized landmark, then processor unit  110  can conclude that the observed landmark is the expected recognized landmark. 
     In some embodiments, after identifying a recognized landmark, processing unit  110  may retrieve information associated with the recognized landmarks. The information may include, for example, positional information of the recognized landmarks. In some embodiments, the information associated with the recognized landmarks may be stored on a remote server, and processing unit  110  may instruct a wireless system of vehicle  200 , which may include a wireless transceiver, to retrieve the information associated with the recognized landmarks. In other cases, the information may already reside on vehicle  200  (e.g., within a local map from sparse data map  800  received during navigation or within a sparse data map  800  preloaded into memory of vehicle  200 ). In some embodiments, this positional information may be used to calibrate one or more indicators of speed of an autonomous vehicle (e.g., one or more speed sensors of vehicle  200 ). 
       FIG. 70  is a flowchart showing an exemplary process  7000  for calibrating a speed of vehicle  200  consistent with disclosed embodiments. At step  7002 , processing unit  110  may receive from an image capture device  122  a plurality of images representative of an environment of vehicle  200 . In some embodiments, images may be captured at different times by image capture device  122  (e.g., images may be captured many times per second, for example). In some embodiments, vehicle  200  may include a plurality of image capture devices (e.g., image capture devices  122  and  124  of vehicle  200 ), and processing unit  110  may receive from each image capture device, a plurality of images representative of an environment of vehicle  200 . The plurality of images received from each image capture device may include images captured at different times by one or more of the image capture devices on the vehicle. 
     At step  7004 , processing unit  110  may analyze the plurality of images to identify at least two recognized landmarks present in the images. The two recognized landmarks need not be present in a single image from among the plurality of images. In fact, in many cases, the two recognized landmarks identified in the plurality of images will not appear in the same images. Rather, a first recognized landmark may be identified in a first image received from an image capture device. At a later time, and perhaps many image frames later (e.g., 10s, 100s, or 1000s of image frames later, or more), a second recognized landmark may be identified in another of the plurality of images received from the image capture device. The first recognized landmark may be used to determine a first location S 1  of the vehicle along a target trajectory at time T 1 , and the second recognized landmark may be used to determine a second location S 2  of the vehicle along the target trajectory at time T 2 . Using information such as a measured distance between S 1  and S 2  and knowing a time difference between T 1  and T 2  may enable the processor unit of the vehicle to determine a speed over which the distance between S 1  and S 2  was covered. This speed can be compared to an integrated velocity obtained based on an output of the vehicle&#39;s speed sensor. In some embodiments, this comparison may yield a correction factor needed to adjust/calibrate the vehicle&#39;s speed sensor to match the speed determined based on the S 1  to S 2  speed calculation. 
     Alternatively, or additionally, the processor unit may use an output of the vehicle&#39;s speed sensor to determine a sensor-based distance reading between S 1  and S 2 . This sensor based distance reading can be compared to a calculated distance between S 1  and S 2  in order to determine an appropriate correction factor to calibrate the vehicle&#39;s speed sensor. 
     Processing unit  110  may identify recognized landmarks in a captured image stream according to any of the techniques described elsewhere in the disclosure. For example, processing unit  110  may compare one or more observed characteristics of a potential landmark to characteristics for a recognized landmark stored in sparse data map  800 . Where one or more of the observed characteristics is found to match the stored characteristics, then processing unit  110  may conclude that the observed potential landmark is, in fact, a recognized landmark. Such characteristics may include, among other things, size, shape, location, distance to another recognized landmark, landmark type, condensed image signature, etc. 
     At step  7006 , processing unit  110  may determine, based on known locations of the two recognized landmarks, a value indicative of a distance between the at least two recognized landmarks. For example, as discussed above, processing unit  110  may retrieve or otherwise rely upon information associated with the recognized landmarks after identifying the recognized landmarks. Further, the information may include positional information of the recognized landmarks, and processing unit  110  may compute a distance between the two recognized landmarks based on the retrieved positional information associated with the two landmarks. Positional information may include, for example, global coordinates of each recognized landmark determined, for example, based on an aggregation of position determinations (e.g., GPS based position determinations) made by a plurality of vehicles upon prior traversals along the road segments including the two recognized landmarks. 
     At step  7008 , processing unit  110  may determine, based on an output of at least one sensor associated with the autonomous vehicle, a measured distance between the at least two landmarks. 
     In some embodiments, processing unit  110  may use an odometry technique based on images captured by image capture device  122 , inertial sensors, and/or a speedometer of vehicle  200  to measure the distance between the two recognized landmarks. For example, as noted above, a first position of the vehicle S 1  may be used as a starting point and a second position of the vehicle S 2  may be used as an ending point. These positions may be determined based on images collected of the first and second recognized landmarks, respectively, using techniques described in other sections of the disclosure. The vehicle sensors (e.g., the speedometer) can be used to measure a distance between location S 1  and S 2 . This measured distance may be compared to a calculated distance between locations S 1  and S 2 , for example, along a predetermined target trajectory of the vehicle. 
     In some embodiments, S 1  and S 2  may be selected according to a particular relationship with the recognized landmarks. For example, S 1  and S 2  may be selected as locations where lines extending from the first and second landmarks, respectively, intersect the target trajectory at right angles. Of course, any other suitable relationship may also be used. In such embodiments, where S 2  and S 1  are defined according to a predetermined relationship, a distance between S 2  and S 1  may be known and represented, for example, in sparse data map  800  (e.g., as a distance value to the preceding recognized landmark). Thus, rather than having to calculate a distance between S 1  and S 2 , in such embodiments, this distance value may already be available from sparse data map  800 . As in previous embodiments, the predetermined distance between S 1  and S 2  may be compared to the distance between S 1  and S 2  measured using the vehicle sensors. 
     For example, in some embodiments, measuring the distance between the two landmarks may be done via a GPS device (e.g., position sensor  130 ). For example, two landmarks may be selected, which are distant from each other (e.g., 5 km) and the road between them may be rather straight. A length of that road segment may be measured, for example, by subtracting the GPS coordinates of the two landmarks. Each such coordinate may be measured with an error of a few meters (i.e., the GPS error), but due to the long length of the road segment this may be a relatively small error. 
     At step  7010 , processing unit  110  may determine a correction factor for the at least one sensor based on a comparison of the value indicative of the distance between the at least two recognized landmarks and the measured distance between the at least two landmarks. The correctional factor may be, for example, a ratio of the value indicative of the distance between the at least to recognized landmarks and the measured distance between the at least two landmarks. In some embodiments, the correction factor may be referred to as a calibration factor and may represent a value that may be used to transform the measured distance value based on the vehicle&#39;s sensors into the calculated/predetermined distance value. 
     In an optional step, processing unit  110  may determine a composite correction factor based on a plurality of determined correction factors. Correction factors of the plurality of determined correction factors may be determined based on different set of landmarks. In some embodiments, the composite correction factor is determined by averaging the plurality of determined correction factors or by finding a mean of the plurality of determined correction factors. 
       FIG. 71  is a diagrammatic top view representation of exemplary vehicle  200  including a system for calibrating an indicator of speed of the vehicle consistent with disclosed embodiments. In the example of  FIG. 71 , vehicle  200  is traveling on a first road segment  7102 A.  FIG. 71  also shows a second road segment  7102 B and lane marks  7104 ,  7106 . A road segment is includes any portion of a road. 
     In some embodiments, processing unit  110  may determine a distance along a road segment (e.g., road segments  7102 A or  7102 B) using one or more sensors of vehicle  200 . In one example, processing unit  110  may determine, using one or more sensors of vehicle  200 , a road signature profile associated with the road segment vehicle  200  is traveling on (e.g., road segment  7102 A). Such road signature profile may be associated with any discernible/measurable variation in at least one parameter associated with the road segment. In some cases, such profile may be associated with, for example, variations in surface roughness of a particular road segment, variations in road width over a particular road segment, variations in distances between dashed lines painted along a particular road segment, variations in road curvature along a particular road segment, etc. As discussed above,  FIG. 11D  shows exemplary road signature profile  1160 . While a road signature profile may represent any of the parameters mentioned above, or others, in one example, the road signature profile may represent a measure of road surface roughness, as obtained, for example, by monitoring one or more sensors providing outputs indicative of an amount of suspension displacement as vehicle  200  travels on first road segment  7102 A. Alternatively, road signature profile  1160  may represent variation in road width, as determined based on image data obtained via image capture device  122  of vehicle  200  traveling on first road segment  7102 A. Such profile may be useful, for example, in determining a particular location of an autonomous vehicle relative to a particular target trajectory. That is, as it traverses a road segment, an autonomous vehicle may measure a profile associated with one or more parameters associated with the road segment. If the measured profile can be correlated/matched with a predetermined profile that plots the parameter variation with respect to position along the road segment, then the measured and predetermined profiles may be used (e.g., by overlaying corresponding sections of the measured and predetermined profiles) in order to determine a current position along the road segment and, therefore, a current position relative to a target trajectory for the road segment. A distance along a road segment may be determined based on a plurality of positions determined along a road segment. 
       FIG. 72  is a flowchart showing exemplary process  7200  for calibrating an indicator of speed of vehicle  200  consistent with disclosed embodiments. In some embodiments, vehicle  200  may calibrate the indicator of speed of vehicle  200  by calculating a correction factor based on a distance determined along the road segment and a distance value received via the wireless transceiver. That is, rather than determining positions S 1  and S 2  based on landmarks and then calculating a distance between positions S 1  and S 2 , a distance value for a predetermined portion of a road segment may be received via sparse data map  800  (e.g., via a wireless transceiver). 
     At step  7204 , processing unit  110  may receive, via a wireless transceiver, a distance value associated with the road segment. In one example, the wireless transceiver may be a 3GPP-compatible or an LTE-compatible transceiver. The distance value associated with the road segment stored on the remote server may be determined based on prior measurements made by a plurality of measuring vehicles. For example, a plurality of vehicles may have previously traveled on the same road segment in the past and uploaded the determined distance values associated with the road segment (e.g., between two or more predetermined reference points, landmarks, etc.) to the remote server. The distance value associated with the road segment stored on the remote server may be an average of the distance values determined by the plurality of measuring vehicles. 
     In some embodiments, the distance value associated with the road segment stored on the remote server may be determined based on prior measurements made by at least  100  measuring vehicles. In other embodiments, the distance value associated with the road segment stored on the remote server may be determined based on prior measurements made by at least  1000  measuring vehicles. 
     At step  7206 , processing unit  110  may determine a correction factor for the at least one speed sensor based on the determined distance along the road segment and the distance value received via the wireless transceiver. The correctional factor may be, for example, a ratio of the distance along the road segment determined using a sensor and the distance value received via the wireless transceiver. And, the correction factor may represent a value that may be used to transform the measured distance value based on the vehicle&#39;s sensors into the received/predetermined distance value. 
     In an optional step, processing unit  110  may determine a composite correction factor based on a plurality of determined correction factors. Correction factors of the plurality of determined correction factors may be determined based on different landmarks. In some embodiments, the composite correction factor is determined by averaging the plurality of determined correction factors or by finding a mean of the plurality of determined correction factors. 
     Determining Lane Assignment Based on Recognized Landmark Location 
     In addition to determining a lane assignment based on analysis of a camera output (e.g., seeing additional lanes to the right and/or left of a current lane of travel for the vehicle), the system may determine and/or validate a lane assignment based on a determined lateral position of recognized landmarks relative to the vehicle. 
       FIG. 73  is a diagrammatic illustration of a street view of an exemplary road segment, consistent with disclosed embodiments. As shown in  FIG. 73 , road segment  7300  may include a number of components, including road  7310 , lane marker  7320 , landmarks  7330 ,  7340 , and  7350 , etc. In addition to the components depicted in exemplary road segment  7300 , a road segment may include other components, including fewer or additional lanes, landmarks, etc., as would be understood by one of ordinary skill in the art. 
     In one embodiment, road segment  7300  may include road  7310 , which may be divided by one or more lane markers  7320  into two or more lanes. Road segment  7300  may also include one or more vehicles, such as vehicle  7360 . Moreover, road segment  7300  may include one or more landmarks, such as landmarks  7330 ,  7340 , and  7350 . In one embodiment, such as shown in  FIG. 73 , landmarks may be placed alongside road  7310 . Landmarks placed alongside road  7310  may include, for example, traffic signs (e.g., speed limit signs, such as landmarks  7330  and  7340 ), mile markers (e.g., landmark  7350 ), billboards, exit signs, etc. Landmarks may also include general purpose signs (e.g., non-semantic signs relating to businesses or information sources, etc.). Alternatively, landmarks may be placed on or above road  7310 . Landmarks placed on or above road  7310  may include, for example, lane markers (e.g., lane marker  7320 ), reflectors, exit signs, marquees, etc. Landmarks can also include any of the examples discussed elsewhere in this disclosure. 
       FIG. 74  is a diagrammatic illustration of a birds-eye view of an exemplary road segment, consistent with disclosed embodiments. As shown in  FIG. 74 , exemplary road segment  7400  may include a number of components, including road  7405 , lane marker  7410 , vehicle  7415 , traversed path  7420 , heading  7425 , predicted path  7430 , predetermined road model trajectory  7435 , landmarks  7440 ,  7455 , and  7470 , direct offset distances  7445 ,  7460 , and  7475 , and lateral offset distances  7450  and  7465 . In addition to the components depicted in exemplary road segment  7300 , a road segment may include other components, including fewer or additional lanes, landmarks, and vehicles, as would be understood by one of ordinary skill in the art. 
     In one embodiment, road segment  7400  may include road  7405 , which may be divided by one or more lane markers  7410  into two or more lanes. Road segment  7300  may also include one or more vehicles, such as vehicle  7415 . Moreover, road segment  7400  may include one or more landmarks, such as landmarks  7440 ,  7455 , and  7470 . 
     In one embodiment, vehicle  7415  may travel along one or more lanes of road  7405  in a path. The path that vehicle  7415  has already traveled is represented in  FIG. 74  as traversed path  7420 . The direction in which vehicle  7415  is headed is depicted as heading  7425 . Based on the current location of vehicle  7145  and heading  7425 , among other factors, a path that vehicle  7415  is expected to travel, such as predicted path  7430 , may be determined.  FIG. 74  also depicts predetermined road model trajectory  7435 , which may represent an ideal path for vehicle  7415 . 
     In one embodiment, direct offset distances  7445 ,  7460 , and  7475  may represent the distance between vehicle  7415  and landmarks  7440 ,  7455 , and  7470 , respectively. Lateral offset distances  7450  and  7465  may represent the distance between vehicle  7415  and the landmarks  7440  and  7455  when vehicle  7415  is directly alongside those landmarks. 
     For example, two techniques may be used to calculate the number of lanes based on the lateral distance estimation between the host vehicle and the landmark. As a first example, a clustering technique may be used. Using mean-shift clustering, the system may calculate the number of lanes and the lane assignment for each drive. Next, for enriching the number of observations and to provide observations from each lane, the system may add observations for the adjacent lanes (e.g., if the lanes&#39; DNN networks decided there are such lanes). Next, the system may determine the road width and splitting it into lanes based on the calculated lane width. As a second example, in another technique, based on sightings of vehicles where the lanes&#39; DNN network determined they are either on the extreme (left or right) lane or on the one adjacent to it, the system may create a set of estimations of the lateral distance between the land mark and the extreme left or right lane mark. Next, using either a voting or a least squares mechanism, the system may determine an agreed distance estimation between the land mark and the road edges. Next, from the distance estimates to the road edges, the system may extract the road width, and determine the number of lanes by dividing the road width by the median lane width observed in the drives. The system may assign a lane to each drive based on which bin the observed distance between the host. 
       FIG. 75  is a flowchart showing an exemplary process  7500  for determining a lane assignment for a vehicle (which may be an autonomous vehicle) along a road segment, consistent with disclosed embodiments. The steps associated with this exemplary process may be performed by the components of  FIG. 1 . For example, the steps associated with the process may be performed by application processor  180  and/or image processor  190  of system  100  illustrated in  FIG. 1 . 
     In step  7510 , at least one processor receives from a camera at least one image representative of an environment of the vehicle. For example, image processor  128  may receive one or more images from one or more of cameras  122 ,  124 , and  126  representing an environment of the vehicle. Image processor  128  may provide the one or more images to application processor  180  for further analysis. The environment of the vehicle may include the area surrounding the exterior of the vehicle, such as the road segment and any signs, buildings, or landscaping along the road segment. In one embodiment, the environment of the vehicle includes the road segment, a number of lanes, and the at least one recognized landmark. 
     In step  7520 , the at least one processor analyzes the at least one image to identify at least one recognized landmark. In one embodiment, the at least one recognized landmark includes at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost, etc. For example, the at least one recognized landmark may include landmarks  7330 ,  7340 , and  7350 , each of which is a traffic sign. In particular, landmarks  7330  and  7340  are speed limit signs, and landmark  7350  is a mile marker sign. In another embodiment, the at least one recognized landmark includes a sign for a business. For example, the at least one recognized landmark may include a billboard advertisement for a business or a sign marking the location of a business. 
     In step  7530 , the at least one processor determines an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark. In some embodiments, the determination of the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark may be based on a known position of the at least one recognized landmark. The known position of the at least one recognized landmark may be stored, for example, in memory  140  or map database  160  (e.g., as part of sparse map  800 ). 
     In step  7540 , the at least one processor determines a lane assignment of the vehicle along the road segment based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark. For example, the at least one processor may determine which lane the vehicle is traveling in based the indicator of lateral offset distance. For example, a lane assignment may be determined based on knowledge of a lateral distance from the recognized landmark to a lane edge closest to the recognized landmark, to any lane edges present on the road, to a target trajectory associated with a road segment, or to multiple target trajectories associate with the road segment, etc. The determined indicator of lateral offset distance between the recognized landmark and the host vehicle may be compared to any of these quantities, among others, and then used to determine a current lane assignment based on one or more arithmetic and/or trigonometric calculations. 
     In one embodiment, the at least one recognized landmark includes a first recognized landmark on a first side of the vehicle and a second recognized landmark on a second side of the vehicle and wherein determination of the lane assignment of the vehicle along the road segment is based on a first indicator of lateral offset distance between the vehicle and the first recognized landmark and a second indicator of lateral offset distance between the vehicle and the second recognized landmark. The lane assignment may be determined based on a ratio of the first indicator of lateral offset distance to the second indicator of lateral offset distance. For example, if the vehicle is located 20 feet from a landmark posted on the left edge of the road and 60 feet from a landmark posted on the right edge of the road, then the lane assignment may be determined based on this ratio, given information on the number of lanes on the road segment or lane width. Alternatively, the lane assignment may be calculated separately based on the indicators of lateral offset distance between the vehicle and the first and second recognized landmarks, and these separate calculations may be checked against one another to verify that the determined lane assignment(s) are correct. 
     Super Landmarks as Navigation Aids 
     The system may navigate by using recognized landmarks to aid in determining a current location of an autonomous vehicle along a road model trajectory. In some situations, however, landmark identity may be ambiguous (e.g., where there is a high density of similar types of landmarks). In such situations, landmarks may be grouped together to aid in their recognition. For example, distances between landmarks within a group of landmarks may be used to create a super landmark signature to aid in positive identification of the landmarks. Other characteristics, such as landmark sequences within a group of landmarks, may also be used. 
       FIG. 76  is an illustration of a street view of an exemplary road segment, consistent with disclosed embodiments. As shown in  FIG. 76 , road segment  7600  may include a number of components, including road  7610 , lane marker  7620 , vehicle  7630 , and landmarks  7640 ,  7650 ,  7660 ,  7670 , and  7680 . In addition to the components depicted in exemplary road segment  7600 , a road segment may include other components, including fewer or additional lanes, landmarks, and vehicles, as would be understood by one of ordinary skill in the art. 
     In one embodiment, road segment  7600  may include road  7610 , which may be divided by one or more lane markers  7620  into two or more lanes. Road segment  7600  may also include one or more vehicles, such as vehicle  7630 . Moreover, road segment  7600  may include one or more landmarks, such as landmarks  7640 ,  7650 ,  7660 ,  7670 , and  7680 . In one embodiment, landmarks may be assigned to structures/objects associated with road  7610  (e.g., landmarks  7670  and  7680 ). Landmarks along road  7610  may include, for example, traffic signs (e.g., mile markers, such as landmark  7670 ), billboards (e.g., landmark  7680 ), lane markers (e.g., landmark  7620 ), reflectors, traffic signs (e.g., exit signs, such as landmarks  7640 ,  7650 , and  7660 ), marquees, etc. Landmarks identified or otherwise represented in sparse data map  800  may be referred to as recognized landmarks. 
     Some areas, especially in urban environments, may have high densities of recognized landmarks. Thus, in some cases, distinguishing between certain recognized landmarks may be difficult based on comparisons based solely on landmark size, shape, type, indexed location, etc. To further aid in identifying one or more recognized landmarks from within images captured of a vehicle&#39;s environment, a group of two or more landmarks may be designated as a super landmark. Such a super landmark may offer additional characteristics that may aid in identifying or verifying one or more recognized landmarks (e.g., from among the group of landmarks). 
     In  FIG. 76 , for example, a super landmark may be formed from the group consisting of landmarks  7640 ,  7650 ,  7660 ,  7670 , and  7680 , or some subset of two or more of those landmarks. By grouping two or more landmarks together, the probability of accurately identifying constituent landmarks from a distant vantage point may be increased. 
     A super landmark may be associated with one or more characteristics, such as distances between constituent landmarks, a number of landmarks in the group, an ordering sequence, one or more relative spatial relationships between the members of the landmark group, etc. Moreover, these characteristics may be used to generate a super landmark signature. The super landmark signature may represent a unique form of identifying the group of landmarks or even a single landmark within the group. 
       FIG. 77A  is an illustration of birds-eye view of an exemplary road segment, consistent with disclosed embodiments. As shown in  FIG. 77 , exemplary road segment  7700  may be associated with a number of components, including road  7705 , lane marker  7710 , vehicle  7715 , traversed path  7720 , predetermined road model trajectory  7725 , landmarks  7730 ,  7735 ,  7740 ,  7745 , and  7750 , lateral offset vector  7755 , and direct offset vector  7760 . In addition to the components depicted in exemplary road segment  7700 , a road segment may be associated with other components, including fewer or additional lanes, landmarks, and vehicles, as would be understood by one of ordinary skill in the art. 
     In one embodiment, road segment  7700  may include road  7705 , which may be divided by one or more lane markers  7710  into two or more lanes. Road segment  7700  may also include one or more vehicles, such as vehicle  7715 . Moreover, road segment  7700  may include one or more landmarks, such as landmarks  7730 ,  7735 ,  7740 ,  7745 , and  7750 . 
     In one embodiment, vehicle  7715  may travel along one or more lanes of road  7705  in a path. The path that vehicle  7715  has already traveled is represented in  FIG. 77  as traversed path  7720 .  FIG. 77  also depicts predetermined road model trajectory  7725 , which may represent a target path for vehicle  7715 . 
     In one embodiment, a direct offset vector may be a vector connecting vehicle  7715  and a landmark. For example, direct offset vector  7760  may be a vector connecting vehicle  7715  and landmark  7730 . The distance between vehicle  7715  and a landmark may be equivalent to the magnitude of direct offset vector connecting vehicle  7715  with the landmark. A lateral offset vector may be a vector connecting vehicle  7715  with a point on the side of the road in line with a landmark. The lateral offset distance for a vehicle with respect to a landmark may be equivalent to the magnitude of the lateral offset vector and, further, may be equivalent to the distance between vehicle  7715  and the landmark when vehicle  7715  is directly alongside the landmark. The lateral offset distance between vehicle  7715  and a landmark may be computed by determining a sum of a first distance between the vehicle and the edge of the road on which the landmark is located and a second distance between that edge and the landmark. 
       FIG. 77B  provides a street level view of a road segment including a super landmark made up of four recognized landmarks: a speed limit sign  7790 , a stop sign  7791 , and two traffic lights  7792  and  7793 . Any of the recognized landmarks included in the super landmark group may be identified based on recognition of various relationships between the landmarks included in the group. For example, a sequence, which may be stored in sparse data map  800 , of a speed limit sign at a distance D 1 , followed by a stop sign at a distance D 2 , and two traffic lights at a distance D 3  from a host vehicle (where D 3 &gt;D 2 &gt;D 1 ) may constitute a unique, recognizable characteristic of the super landmark that may aid in verifying speed limit sign  7790 , for example, as a recognized landmark from sparse data map  800 . 
     Other relationships between the members of a super landmark may also be stored in sparse data map  800 . For example, at a particular predetermined distance from recognized landmark  7790  and along a target trajectory associated with the road segment, the super landmark may form a polynomial  7794  between points A, B, C, and D each associated with a center of a member of the super landmark. The segment lengths A-B, B-C, C-D, and D-A may be determined and stored in sparse data map  800  for one or more positions relative to the location of the super landmark. Additionally, a triangle  7795  may be formed by traffic light  7793 , traffic light  7792 , and stop sign  7791 . Again, the lengths of the sides as well as angles of triangle  7795  may be referenced in sparse data map  800  for one or more positions relative to the location of the super landmark. Similar information may be determined and stored for a triangles  7796  (between points A, C, and D) and  7797  (between points A-B-C). Such angles, shapes, and segment lengths may aid in recognition of a super landmark from a certain viewing location relative to the super landmark. For example, once the vehicle is located at a viewing location for which visual information for the super landmark is included in sparse data map  800 , the processing unit of the vehicle can analyze images captured by one or more cameras onboard the vehicle to look for expected shapes, patterns, angles, segment lengths, etc. to determine whether a group of objects forms an expected super landmark. Upon verifying the recognized super landmark, position determinations for the vehicle along a target trajectory may commence based on any of the landmarks included in a super landmark group. 
       FIG. 78  is a flowchart showing an exemplary process  7800  for autonomously navigating a vehicle along a road segment, consistent with disclosed embodiments. The steps associated with this exemplary process may be performed by the components of  FIG. 1 . For example, the steps associated with the process may be performed by application processor  180  and/or image processor  190  of system  100  illustrated in  FIG. 1 . 
     In step  7810 , at least one processor may receive from a camera at least one image representative of an environment of the vehicle. For example, image processor  128  may receive one or more images from one or more of cameras  122 ,  124 , and  126  representing an environment of the vehicle. Image processor  128  may provide the one or more images to application processor  180  for further analysis. The environment of the vehicle may include the area surrounding the exterior of the vehicle, such as the road segment and any signs, buildings, or landscaping along the road segment. In one embodiment, the environment of the vehicle includes the road segment, a number of lanes, and the at least one recognized landmark. 
     In step  7820 , the at least one processor may analyze the at least one image to identify a super landmark and identify at least one recognized landmark from the super landmark. In one embodiment, the at least one recognized landmark includes at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost. For example, the at least one recognized landmark may include landmarks  7640 ,  7650 ,  7660 , and  7670 , each of which is a traffic sign. In particular, landmarks  7640 ,  7650 , and  7660  are exit signs, and landmark  7670  is a mile marker sign. In another embodiment, the at least one recognized landmark includes a sign for a business. For example, the at least one recognized landmark may include a billboard advertisement for a business (e.g., landmark  7680 ) or a sign marking the location of a business. 
     As noted above, identification of the at least one landmark is based, at least in part, upon one or more landmark group characteristics associated with the group of landmarks. In one embodiment, the one or more landmark group characteristics may include relative distances between members of the group of landmarks. For example, the landmark group characteristics may include information that specifies the distance that separates each landmark in the group from each of the other landmarks in the group. In another embodiment, the one or more landmark group characteristics may include an ordering sequence of members of the group of landmarks. For example, the group of landmarks may be associated with a sequence indicating the order in which the landmarks appear from left to right, front to back, etc., when viewed from the road. In yet another embodiment, the one or more landmark group characteristics may include a number of landmarks included in the group of landmarks. 
     Referring to  FIG. 76  as an example, a landmark group (or super landmark) may consist of landmarks  7640 ,  7650 ,  7660 ,  7670 , and  7680 . This landmark group may be associated with landmark group characteristics, including the relative distances between each landmark and each of the other landmarks in the group, an ordering sequence of landmarks in the group, and a number of landmarks. In the example depicted in  FIG. 76 , the landmark group characteristics may include information that specifies the distance between landmark  7680  and each of landmarks  7640 ,  7650 ,  7660 , and  7670 , the distance between landmark  7640  and each of landmarks  7650 ,  7660 , and  7670 , the distance between landmark  7650  and each of landmarks  7660  and  7670 , and the distance between landmarks  7660  and  7670 . 
     Further, in this example, an ordering sequence may indicate that the order of landmarks in the group from left to right (when viewed from the perspective of a vehicle driving along the road, e.g., vehicle  7630 ) is  7680 ,  7640 ,  7650 ,  7660 , and  7670 . Alternatively or additionally, the ordering sequence may indicate that the order of landmarks in the group from front to back (e.g., earliest to latest traversed in a path along the road) is first  7670 , then  7640 ,  7650 , and  7660 , and last  7680 . Moreover, the landmark group characteristics may specify that this exemplary landmark group includes five landmarks. 
     In one embodiment, identification of the at least one landmark may be based, at least in part, upon a super landmark signature associated with the group of landmarks. A super landmark signature may be a signature for uniquely identifying a group of landmarks. In one embodiment, a super landmark signature may be based on one or more of the landmark group characteristics discussed above (e.g., number of landmarks, relative distance between landmarks, and ordering sequence of landmarks). 
     Once a recognized landmark is identified based on an identified characteristic of the super landmark group, predetermined characteristics of the recognized landmark may be used to assist a host vehicle in navigation. For example, in some embodiments, the recognized landmark may be used to determine a current position of the host vehicle. In some cases, the current position of the host vehicle may be determined relative to a target trajectory from sparse data model  800 . Knowing the current position relative to a target trajectory may aid in determining a steering angle needed to cause the vehicle to follow the target trajectory (for example, by comparing a heading direction to a direction of the target trajectory at the determined current position of the vehicle relative to the target trajectory). 
     A position of the vehicle relative to a target trajectory from sparse data map  800  may be determined in a variety of ways. For example, in some embodiments, a 6D Kalman filtering technique may be employed. In other embodiments, a directional indicator may be used relative to the vehicle and the recognized landmark. For example, in step  7830 , the at least one processor may determine, relative to the vehicle, a directional indicator associated with the at least one landmark. In one embodiment, the directional indicator may include a line or vector connecting the vehicle and the at least one landmark. The directional indicator may indicate the direction in which the vehicle would have to travel to arrive at the at least one landmark. For example, in the exemplary embodiment depicted in  FIG. 77 , direct offset vector  7760  may represent a directional indicator associated with landmark  7730  relative to vehicle  7715 . 
     In step  7840 , the at least one processor may determine an intersection of the directional indicator with a predetermined road model trajectory associated with the road segment. In one embodiment, the predetermined road model trajectory may include a three-dimensional polynomial representation of a target trajectory along the road segment. The target trajectory may include an ideal trajectory for the vehicle for a specific location along the road segment. In one embodiment, the at least one processor may further be programmed to determine a location along the predetermined road model trajectory based on a vehicle velocity. For example, the at least one processor may access information the location and velocity of the vehicle at a specific time, compute an estimated distance traveled based on the velocity and time passed since the vehicle was at that location, and identify a point along the predetermined road model trajectory that is the estimated distance beyond the previously observed location. 
     In step  7850 , the at least one processor may determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined intersection. In one embodiment, determining an autonomous steering action for the vehicle may include comparing a heading direction of the vehicle to the predetermined road model trajectory at the determined intersection. In one embodiment, the autonomous steering action for the vehicle may include changing the heading of the vehicle. In another embodiment, the autonomous steering action for the vehicle may include changing the speed of the vehicle by applying the gas or brake to accelerate or decelerate, respectively. 
     Adaptive Autonomous Navigation 
     In some embodiments, the disclosed systems and methods may provide adaptive autonomous navigation and update a sparse map. For example, the disclosed systems and methods may adapt navigation based on user intervention, provide adapt navigate based on determinations made by the system (e.g., a self-aware system), adapt a road model based on whether observed conditions on a road are transient or non-transient (e.g., an adaptive road model manager), and manage a road model based on selective feedback received from one or more systems. These adaptive systems and methods are discussed in further detail below. 
     Adaptive Navigation Based on User Intervention 
     In some embodiments, the disclosed systems and methods may involve adaptive navigation based on user intervention. For example, as discussed in earlier sections, a road model assembled based upon input from existing vehicles may be distributed from a server (e.g., server  1230 , discussed earlier) to vehicles. Based on feedback received from autonomous vehicles, the system may determine whether one or more updates (e.g., adaptations to the model) are needed to the road model to account for changes in road situations, for example. For example, in some embodiments, a user may intervene to alter a maneuver of a vehicle (which may be an autonomous vehicle) while the vehicle is traveling on a roadway according to the road model. An altered maneuver of the vehicle based on user intervention may be made in contradistinction to override predetermined vehicular trajectory instructions provided by the road model. Further, the disclosed systems and methods may capture and store navigational situation information about the situation in which the override occurred and/or send the navigational situation information from the vehicle to the server over one or more networks (e.g., over a cellular network and/or the Internet, etc.) for analysis. As discussed herein, navigational situation information may include one or more of a location of a vehicle, a distance of a vehicle to a recognized landmark, an observed condition, a time of day, an image or a video captured by an image capture device of a vehicle, or any other suitable informational source regarding a navigational situation. 
       FIG. 79A  illustrates a plan view of vehicle  7902  traveling on a roadway  7900  approaching wintery and icy road conditions  7930  at a particular location consistent with disclosed embodiments. Vehicle  7902  may include a system that provides navigation features, including features that adapt navigation based on user intervention. Vehicle  7902  may include components such as those discussed above in connection with vehicle  200 . For example, as depicted, vehicle  7902  may be equipped with image capture devices  122  and  124 ; more or fewer image capture devices (including cameras, for example) may be employed. 
     As shown, roadway  7900  may be subdivided into lanes, such as lanes  7910  and  7920 . Lanes  7910  and  7920  are shown as examples; a given roadway  7900  may have additional lanes based on the size and nature of the roadway, for example, an interstate highway. In the example of  FIG. 79A , vehicle  7902  is traveling in lane  7910  according to instructions derived from the road model (e.g., a heading direction along a target trajectory) and approaching wintery and icy road conditions  7930  at a particular vehicle location as identified by, e.g., position sensor  130 , a temperator sensor, and/or an ice sensor. Where a user intervenes in order to override autonomously generated steering instructions (e.g., those enabling the vehicle to maintain a course along the target trajectory) and alter the course of the vehicle  7902  traveling in lane  7910  (e.g., to turn due to the icy conditions), processing unit  110  may store navigational situation information and/or send the navigational situation information to a server of the road model system for use in making a possible update. In this example, the navigational situation information may include a location of the vehicle identified by position sensor  130  or based on a landmark-based determination of position along a target trajectory, an image captured by an image capture device included in the vehicle depicting the vehicle&#39;s environment, an image stream (e.g., a video), sensor output data (e.g., from speedometers, accelerometers, etc.). 
     In some embodiments, processing unit  110  may send the navigational situational information from the vehicle to the server via a wireless data connection over one or more networks (e.g., over a cellular network and/or the Internet, etc.). The server side may analyze the received information (e.g., using automated image analysis processes) to determine whether any updates to sparse data model  800  are warranted based on the detected user intervention. In this example, the server may recognize the presence of wintery or icy road conditions in the images (a temporary or transient condition) and, therefore, may determine not to change or update the road model. 
       FIG. 79B  illustrates a plan view of vehicle  7902  traveling on a roadway approaching a pedestrian consistent with disclosed embodiments. In the example of  FIG. 79B , vehicle  7902  is driving in lane  7910  of roadway  7900  with a pedestrian  7922 . As shown, pedestrian  7922  may suddenly become positioned directly in the roadway  7900  crossing either lane  7910  or  7920 . In this example, when a user intervenes to override the road model in order to avoid the pedestrian and alter the maneuver of the vehicle  7902  traveling in lane  7910  along a target trajectory associated with the road segment, navigational situation information including a position of the vehicle along a target trajectory for a road segment (e.g., determined based on a distance d 1  to a recognized landmark, such as speed limit sign  7923 ), video or images including capturing conditions of the vehicle&#39;s surroundings during the user intervention, sensor data, etc. In example shown in  FIG. 49B , given the temporary nature of a crossing pedestrian the server may determine not change or update the road model. 
     Although the example shown in  FIG. 79B  depicts speed limit sign  7923 , other recognized landmarks (not shown) may be used. Landmarks may include, for example, any identifiable, fixed object in an environment of at least one road segment or any observable characteristic associated with a particular section of the road segment. In some cases, landmarks may include traffic signs (e.g., speed limit signs, hazard signs, etc.). In other cases, landmarks may include road characteristic profiles associated with a particular section of a road segment. Further examples of various types of landmarks are discussed in previous sections, and some landmark examples are shown in  FIG. 10 . 
       FIG. 79C  illustrates a plan view of a vehicle traveling on a roadway in close proximity to another vehicle consistent with disclosed embodiments. In the example of  FIG. 79C , two vehicles  7902   a  and  7902   b  are driving in lane  7910  of roadway  7900 . As shown, vehicle  7902   b  has suddenly driven directly in front of vehicle  7902   a  in lane  7910  of roadway  7900 . Where a user intervenes to override the road model and alter the course of the vehicle  7902   a  traveling in lane  7910  (e.g., to turn due to the proximate vehicle), navigational situation information may be captured and stored in memory (e.g., memory  140 ) and/or sent to a server (e.g., server  1230 ) for making a possible update to the road model. For example, in this example, the navigational situation information may include a location of vehicle  7902   a.  The navigational situation information may further include one more images depicting the environment of vehicle  7902  at the time of the user intervention. Given the temporary nature of another contiguous or proximate vehicle, however, the server may not change or update the road model. 
       FIG. 79D  illustrates a plan view of a vehicle traveling on a roadway in a lane that is ending consistent with disclosed embodiments. Vehicle  7902  may receive from image capture devices  122  and  124  at least one environmental image of a turning roadway  7900  representative of a lane  7910  ending. Lane  7910  may be ending based on a recent change to lane  7910  resulting an abrupt shortening distance of d 2 . For example, the lane may be ending as a result of recently positioned concrete barriers at the site of a construction zone. As a result of this unexpected shortening, a user may intervene to change the course of vehicle  7902  in view of the change to lane  7910 . As will be discussed in more detail in another section, it is also possible for processing unit  110  to recognize the ending lane (e.g., based on captured images of concrete barriers in front of the vehicle) and automatically adjust the course of the vehicle and send navigational situation information to the server for use in possible updates to sparse data model  800 . As a result of the user intervention, the system may measure distances (such as c 1  and c 2 ). For example, distances c 1  and c 2  may represent the distance from a side of vehicle  7902  to the edge of lane  7910 , be it lane constraint  7924  or the dashed center line in the middle of roadway  7900  dividing lanes  7910 / 7920 . In other embodiments, a distance may be measured to lane constraint  7924  on the far side of lane  7920  (not shown). In addition to distances c 1  and c 2  described above, in some embodiments, processing unit  110  may further be configured to calculate distances w 1  and w 2  and midpoint m of lane  7910  relative to one or more lane constraints associated with that lane. When summed together, distances w 1  and w 2  equal measurement w as shown in  FIG. 79D . 
     In this example, where a user intervenes to override the road model to alter the maneuver of the vehicle  7902  traveling in lane  7910 , navigational situation information including distances c 1 , c 2 , d 2 , w 1 , and w 2  to a lane constraint  7924  may be captured and stored in memory (e.g., memory  140 ) and/or sent to the server for making a possible update to the road model. Of course, other navigational situation information may also be collected and sent to a server for review. Such information may include sensor outputs, captured images/image streams, a position of the vehicle, etc. Given the permanent or semi-permanent nature of an ending lane marked by concrete barriers, the server may decide to change or update the road model. Accordingly, vehicles may receive an updated to the road model that causes the vehicles to follow a new or updated target trajectory for the road segment upon approaching new lane constraint  7924 . 
       FIG. 80  illustrates a diagrammatic side view representation of an exemplary vehicle  7902  including system  100  consistent with the disclosed embodiments. As is shown, vehicle  7902  may be limited by a vision inhibitor such as glare  8002  from the sun and/or a malfunctioning lamp  8004 . Vehicle  7902  is additionally depicted with sensors  8006  and system  100  is capable of determining whether or not it is day or night. The sensors  8006  may include, for example, an IR sensor and/or an accelerometer. For example, where a user intervenes to override the road model to move vehicle  7902  to avoid a glare produced by sun, the processing unit  110  may capture navigational situation information reflecting a time of day and/or the presence of glare. Processing unit  110  may store the navigational situational information and/or transmit the navigational situation information to a server for storage and/or analysis. Given the temporary nature of the glare, the server may decide not to change or update the road model. 
       FIG. 81  illustrates an example flowchart representing a method for adaptive navigation of a vehicle based on user intervention overriding the road model consistent with the disclosed embodiments. In particular,  FIG. 81  illustrates a process  8100  for adaptive navigation of a vehicle consistent with disclosed embodiments. Steps of process  8100  may be performed by processing unit  110  of system  100 . Process  8100  may allow for user input and a navigational maneuver based on analysis of an environmental image. Where there is user input that deviates from a navigational maneuver prescribed by the road model, the maneuver may be altered according to the user input and the conditions surrounding the user input may be captured and stored and/or sent to a server for making a possible update to the road model. 
     At step  8110 , processing unit  110  may receive at least one environmental image of an area forward of vehicle  7902 . For example, the image may show one or more recognized landmarks. As discussed elsewhere in detail, a recognized landmark may be verified in the captured image and used to determine a position of the vehicle along a target trajectory for a particular road segment. Based on the determined position, the processing unit  110  may cause one or more navigational responses, for example, to maintain the vehicle along the target trajectory. 
     At step  8112 , processing unit  110  may include determining a navigational maneuver responsive to an analysis of at least one environmental image of an area forward of vehicle  7902 . For example, based on the landmark-based position determination for the vehicle along the target trajectory, the processing unit  110  may cause one or more navigational responses to maintain the vehicle along the target trajectory. 
     At step  8114 , process  8100  may cause vehicle  7902  to initiate the navigational maneuver. For example, processing unit  110  may send instructions to one or more systems associated with vehicle  7902  to initiate the navigational maneuver and may cause vehicle  7902  to drive according to a predetermined trajectory along roadway  7900 . Consistent with the disclosed embodiments, an initiation instruction may be sent to a throttling system  220 , braking system  230 , and/or steering system  240 . 
     At step  8116 , the system may receive a user input that differs from one or more aspects of the navigational maneuver implemented by processing unit  110  based on sparse data map  800 . For example, a user input to one or more of throttling system  220 , braking system  230 , and/or steering system  240  may differ from an initiated maneuver and cause an override to alter the maneuver based on the received user input. 
     Based on detection of a user override or intervention condition, processing unit  110  may collect navigational situation information relating the vehicle and the user input at the time before, during, and/or after the user intervention. For example, processing unit  110  may receive information relating to the user input, including information specifying at least one of a degree of turn, an amount of acceleration, and an amount of braking of a vehicle  7902 , etc. caused by the user intervention (step  8118 ). 
     At step  8118 , processing unit  110  may determine additional navigational situation information relating to vehicle user input. The navigational situation information may include, for example, a location of the vehicle, a distance to one or more recognized landmarks, a location determined by position sensor  130 , one or more images captured by an image capture device of vehicle  7902 , sensor outputs etc. 
     At step  8020 , processing unit  110  may store the navigational situation information into memory  140  or  150  of system  100  in association with information relating to the user input. Alternatively, in other embodiments, the navigational situation information may be transmitted to a server (e.g., server  1230 ) for use in a making a possible update to the road model. Alternatively, in still yet other embodiments, system  100  may not store the navigational situation information if system  100  determines that the navigation situation information is associated with a condition that may not occur in the future (e.g., a special condition or a transient condition), such as related to pedestrian or an animal moving in front of vehicle  7902 . System  100  may determine that such conditions do not warrant further analysis and this may determine to not store the navigational situation information associated with the transient condition. 
     Self-Aware System for Adaptive Navigation 
     In some embodiments, the disclosed systems and methods may provide a self-aware system for adaptive navigation. For example, a server (e.g., server  1230 ), may distribute a road model to vehicles. Based on feedback received from autonomous vehicles, the system may determine whether one or more updates (e.g., adaptations to the model) are needed to the road model to account for changes in road situations. For example, in some embodiments, a vehicle (which may be an autonomous vehicle) may travel on a roadway based on the road model and may make use of observations made by the self-aware system in order to adjust a navigational maneuver of the vehicle based on a navigational adjustment condition. As discussed herein, a navigational adjustment condition may include any observable or measurable condition in an environment of a vehicle. The system may determine a navigational maneuver for the vehicle based, at least in part, on a comparison of a motion of the vehicle with respect to a predetermined model representative of a road segment. The system may receive from a camera, at least one image representative of an environment of the vehicle, and then determine, based on analysis of the at least one image, an existence in the environment of the vehicle of a navigational adjustment condition. Based on this analysis, the system may, without user intervention, cause the vehicle to adjust the navigational maneuver based on the existence of the navigational adjustment condition. The system may store information relating to the navigational adjustment condition, including, for example, data, an image, or a video related to the navigational adjustment condition. And, the system may transmit the stored information to one or more server-based systems for analysis and/or determination of whether an update to the road model is needed. 
     In some embodiments, the system onboard the vehicle or in the cloud may identify an object or a condition that is estimated to be associated with the navigational adjustment condition. The system may establish whether the navigational adjustment condition is temporary or not and whether the road model should be updated or not. The system may also establish in this way whether to collect further information from future traversals of the same area, location, road, region, etc. 
       FIG. 82A  illustrates a plan view of a vehicle traveling on a roadway with a parked car consistent with disclosed embodiments. In particular,  FIG. 82A  illustrates vehicle  7902   a  traveling according to a three-dimensional spline representative of a predetermined path of travel  8200  (e.g., a target trajectory) along roadway  7900  where a second vehicle  7902   c  is parked directly in front of vehicle  7902   a.  Vehicle  7902   a  may include a system that provides navigation features, including features that allow for navigation based on user input. Vehicle  7902   a  may include components such as those discussed above in connection with vehicle  200 . For example, as depicted, vehicle  7902   a  may be equipped with image capture devices  122  and  124 ; more or fewer image capture devices (including cameras, for example) may be employed. 
     As shown, roadway  7900  may be subdivided into lanes, such as lanes  7910  and  7920 . Vehicle  7902   a  may receive from one or more of image capture devices  122  and  124  at least one environmental image including an image of a parked vehicle  7902   c.  In the example of  FIG. 82A , vehicle  7902   a  is traveling along path  8200  in lane  7910  according to instructions derived from the road model (e.g., a heading direction along a target trajectory) and approaching parked vehicle  7902   c.  Where the system overrides autonomously generated steering instructions (e.g., those enabling the vehicle to maintain a course along the target trajectory) to adjust a maneuver of vehicle  7902   a  due to a navigational adjustment condition, e.g., to avoid parked vehicle  7902   c,  navigational adjustment condition information may be captured and stored in memory (e.g., memory  140 ) and/or sent to a server (e.g., server  1230 ) for making a possible update to the road model. In this example, the navigational adjustment condition information may include a location of vehicle  7902   c  when the autonomous navigational change (e.g., made by the self-aware system) was made. The vehicle position may be identified by position sensor  130  or based on a landmark-based determination of position along a target trajectory. Other navigational condition information may be included in one or more images captured by an image capture device included in vehicle  7902   c  depicting the vehicle&#39;s environment (e.g., an image including parked vehicle  7902   c ), an image stream (e.g., a video), and/or sensor output data (e.g., from speedometers, accelerometers, etc.). 
     In some embodiments, processing unit  110  may send the navigational situational information from the vehicle to the server via a wireless data connection over one or more networks (e.g., over a cellular network and/or the Internet, etc.). The server side may analyze the received information (e.g., using automated image analysis processes) to determine whether any updates to sparse data model  800  are warranted based on the detected system intervention. In this example, the server may recognize the presence of the parked car in or near a target trajectory of the host vehicle and determine that the parked car represents a temporary or transient condition. Therefore, the server may determine not to change or update the road model. However, in some embodiments, based on the location of vehicle  7902   a,  the server may determine that the parked car is located in a residential area and therefore may change or update the road model due to the likelihood of vehicles being parked along the shoulder of the road. Furthermore, in some embodiments, system  100  onboard the vehicle may classify an object or condition and system  100  may determine whether or not to change or update the road model. 
       FIG. 82B  illustrates a plan view of a vehicle traveling on a roadway along a target trajectory associated with the road segment consistent with the disclosed embodiments. Vehicle  7902  may receive from image capture devices  122  and  124  at least one environmental image of a turning roadway  7900  representative of a lane  7910  ending. This change in lane  7910  may be due to recent modifications to a road, and thus may not be yet reflected in the sparse data model  800 . 
     In this example, the vehicle systems may recognize the ending lane and override navigation according to the road model in order to adjust a maneuver of the vehicle  7902  traveling along path  8200  in lane  7910 . For example, processing unit  110 , using one or more images captured with cameras aboard the vehicle may recognize a blockage in the path along the target trajectory associated with the road segment. Processing unit  110  may adjust steering of the vehicle to leave a path indicated by the target trajectory in order to avoid lane constraint  7924 . As a result of the system generated navigational adjustment, navigational adjustment condition information (e.g., including the existence of an ending of lane  7910 , any of distances c 1 , c 2 , d 2 , r, w 1 , and w 2  etc.) may be stored in memory (e.g., memory  140 ) and/or sent to a server (e.g. server  123 ) for possible update of the road model. In some embodiments, in addition or alternatively, the navigational adjustment condition information may include a location of vehicle  7902  based on data determined by position sensor  130  and/or a position of vehicle  7902  relative to one or more recognized landmarks. 
     The server side may analyze the received information (e.g., using automated image analysis processes) to determine whether any updates to sparse data model  800  are warranted based on the detected system intervention. In some embodiments, the server may or may not update the road model based on the received navigational adjustment condition information. For example, given the permanent nature of an ending lane accompanied by a lane shift, the server may decide it is necessary to change or update the road model. Accordingly, the sever may modify the road model in order to steer or turn to merge at these distances c 1 , c 2 , d 2 , w 1 , and w 2  upon approaching lane constraint  7924 . The model may also be updated based on a received, reconstructed and actual trajectory taken by vehicle  7902  as it navigated past the ending lane. Additionally, rather than aggregating the actual path of vehicle  7902  with other trajectories stored in sparse data model  800  for the particular road segment (e.g., by averaging the path of vehicle  7902  with other trajectories stored in sparse data model  800 ), the target trajectory may be defaulted to the path of vehicle  7902 . That is, because the server may determine that the cause of the navigational change was a non-transient (or semi-permanent) condition, the path of vehicle  7902  may be more accurate for the particular road segment than other trajectories for the road segment collected before the condition existed. The same approach and analysis could also be employed by the server upon receiving navigational modifications based not on control by the self-aware vehicle system, but on user intervention (described above). 
       FIG. 82C  illustrates a plan view of a vehicle traveling on a roadway approaching a pedestrian consistent with disclosed embodiments. In the example of  FIG. 82C , vehicle  7902  is driving in lane  7910  of roadway  7900  with pedestrian  7926 . As shown, pedestrian  7926  may be positioned directly in roadway  7900  or alternatively may be positioned to the side of roadway  7900 . Vehicle  7902  may travel in lane  7910  according to instructions derived based on the road model (e.g., a heading direction along a target trajectory) and may approach pedestrian  7926 . Vehicle  7902  may receive from image capture devices  122  and  124  at least one environmental image including an image of pedestrian  7926 . Where the system intervenes to override the road model to adjust a maneuver of the vehicle  7902  traveling in lane  7910  to avoid pedestrian  7926 , navigational adjustment condition information including, for example, a distance d 1  to a stop sign and/or a capture image depicting pedestrian  7926  may be captured and stored in memory (e.g., memory  140 ) and/or sent to a server (e.g., server  1230 ) for making a possible update to the road model. The server side may analyze the received information (e.g., using automated image analysis processes) to determine whether any updates to sparse data model  800  are warranted based on the detected system intervention. In this example, given the temporary nature of a pedestrian, the server may determine to not change or update the road model. 
     Optionally, in some embodiments, when the cause of the intervention is not confidently ascertained by the system, or when the nature of the cause in not clear or is inherently not constant or stable, the server may issue an alert and/or provide one, two, or more alternative paths or road models. In such an embodiment, the server may cause the system onboard the vehicle to examine the situation on the ground including when the vehicle arrived at the point or area where the deviation or intervention occurred. The server may further provide a location of a suspected and/or verified cause of the intervention, to allow the system to focus on that area. As such, the system may have more time and more information to evaluate the situation. 
       FIG. 82D  illustrates a plan view of a vehicle traveling on a roadway approaching an area of construction consistent with the disclosed embodiments. As shown, vehicle  7902  is traveling a target trajectory associated with the road segment (e.g., according to a three-dimensional spline representative of a predetermined target trajectory  8200 ) along a roadway  7900  where a construction area  8200   d  is located directly in front of vehicle  7902 . Vehicle  7902  may receive from image capture devices  122  and  124  at least one environmental image including an image of construction area  8200   d.  Where the system intervenes to override one or more navigational maneuvers generated based on the road model in order to avoid construction area  8200   d,  navigational adjustment condition information may be stored. Such information may include, for example, the existence of a construction area  8200   d  (e.g., as depicted in one or more captured images). The navigational adjustment condition information may also be sent to a server (e.g. server  120 ) for a making one or more possible updates to sparse data model  800 . In some embodiments, the navigational adjustment condition information may include a location of vehicle  7902  based on, for example, a position sensor  130  and/or a location of a known landmark relative to vehicle  7902  at the time of adjustment. The server side may analyze the received information (e.g., using automated image analysis processes) to determine whether any updates to sparse data model  800  are warranted based on the detected system intervention. In this example, due to the non-transient nature of the roadway construction (where non-transient may refer to a condition likely to exist longer than a predetermined period of time, including, for example, several hours, a day, a week, a month, or more), the server may determine to change or update the road model. 
       FIG. 83  illustrates an example flowchart representing a method for model adaptation based on self-aware navigation of a vehicle consistent with the disclosed embodiments. In particular,  FIG. 83  illustrates a process  8300  that may be performed by processing unit  110  of system  100 . As discussed below, process  8300  may use a road model defining a predetermined vehicle trajectory  8200 . Where a maneuver deviates from navigational maneuvers developed based on the predetermined model vehicle trajectory  8200 , the model and information regarding a navigational adjustment condition may be captured and stored and/or sent to a server (e.g., server  1230 ) for making a possible update to the road model. 
     At step  8310 , processing unit  110  may determine a navigational maneuver based on a comparison of a vehicle position with respect to a predetermined model associated with a road segment. As discussed elsewhere in detail, a recognized landmark may be verified in the captured image and used to determine a position of the vehicle along a target trajectory for a particular road segment. Based on the determined position, the processing unit  110  may cause one or more navigational responses, for example, a navigational maneuver to maintain the vehicle (e.g., steer the vehicle) along the target trajectory. 
     At step  8312 , processing unit  110  may receive an environmental image of an area forward of vehicle  7902 . For example, processing unit  110  may receive an image of an environment of vehicle  7902  that includes a parked vehicle, a lane constraint having a road curvature or turning roadway radius r providing information indicative, for example, of a roadway lane ending, a pedestrian and/or construction area. 
     At step  8314 , processing unit  110  may determine an existence of a navigational adjustment condition. The navigational adjustment condition may be determined responsive to an analysis of at least one environmental image of an area forward of vehicle  7902  and may include, for example, a parked car in front of vehicle  7902   a,  a roadway curvature having turn radius r providing information indicative, for example, of construction area in lane  7910 . These are, of course, examples, and the captured images may include any of a multitude of conditions within an environment of the vehicle that may warrant an adjustment in navigation away from a target trajectory included in sparse data model  800 . 
     At step  8316 , processing unit  110  may cause vehicle  7902  to adjust the navigational maneuver based on the navigational adjustment condition. For example, processing unit  110  may cause vehicle  7902  to change heading directions away from a direction of the target trajectory in order to avoid a parked car, a road construction site, a pedestrian, etc. Consistent with the disclosed embodiments, instructions may be sent to a throttling system  220 , braking system  230 , and/or steering system  240  in order to cause the adjustment to one or more navigational maneuvers generated based on sparse data model  800 . 
     At step  8318 , processing unit  110  may store information relating to the navigational adjustment condition information into memory  140  or  150  of system  100 . Such information may include one or more images captured of the environment of the vehicle at the time of the navigational adjustment that resulted in a departure from the target trajectory of sparse data model  800 . The information may also include a position of the vehicle, outputs of one or more sensors associated with the vehicle, etc. 
     At step  8320 , processing unit  110  may transmit the navigational adjustment condition information to a road model management system (e.g., server  1230 ) for analysis and for potentially updating a predetermined model representative of the roadway. 
     Adaptive Road Model Manager 
     In some embodiments, the disclosed systems and methods may provide an adaptive road model manager. The adaptive road model manager may be provided by a server (e.g., server  1230 ), which may receive data from vehicles and decide whether or not to make an update to the road model if an adjustment from an expected vehicular navigational maneuver was not due to a transient condition. The vehicles may send data to the server regarding navigational departures from the road model using a wireless data connection over one or more networks (e.g., including over a cellular network and/or the Internet). For example, the server may receive from each of a plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver. The server may analyze the navigational situation information and determine, based on the analysis of the navigational situation information, whether the adjustment to the determined navigational maneuver was due to a transient condition. In some embodiments, the server may detect the navigational maneuver from raw data provided by the vehicle (e.g., by processing image data). The server may update the predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition. As discussed herein, a transient condition is any condition expected to change after a predetermined time period (e.g., less than a few hours, a day, or a week or more) such that an update to a road model is not warranted or desirable. Such transient conditions may be expected to no longer be present after the predetermined time period and therefore the server may determine to not change or update to the road model. Conversely, if the server determines the adjustment was not due to a transient condition, the server may determine to update the road model. 
     In some embodiments, when a navigational maneuver is detected, the server may mark the respective area of the road model as being associated with a suspected change. The server may then determine from further updates from the same location or a nearby location (e.g., in some embodiments, “pulling” such updates from vehicles at the location or nearby the location), and may process the data in an attempt to verify the change. When the change is verified, the server may update the model, and may subsequently communicate the updated model of the respective area, replacing the former version of the model. The server may implement a confidence level such that the update occurs when the confidence level is above a certain level. The confidence level may be associated with the type of maneuver, the similarity between two or more maneuvers, identification of a source of the adjustment, a frequency of consistent updates, and the number of ratio of inconsistent updates, environmental conditions, such as weather, urban vs. rural environments, etc. The severity of the cause of the maneuver may also be taken into account when determining the confidence level. If the maneuver is severe (e.g., a sharp turn) and the cause may be associated with a potential weather situation and, in some embodiments, a less restrictive approval process can may be used. 
       FIG. 84A  illustrates a plan view of a vehicle traveling on a roadway with multiple parked cars consistent with the disclosed embodiments. As shown, vehicle  7902   a  is traveling according to a target trajectory (e.g., a three-dimensional spline representative of a predetermined path of travel  8400 ) of a road model along roadway  7900  where another vehicle  7902   c  is parked directly in front of vehicle  7902   a.  Roadway  7900  may be subdivided into lanes, such as lanes  7910  and  7920 . Where either the system or user intervenes to override a navigational maneuver generated based on the road model and adjust a maneuver of the vehicle  7902  traveling along path  8400  in lane  7910  to avoid parked vehicles  7902   c,  navigational situation information including, for example, the existence of parked cars  7902   c  in lane  7910  (e.g., as depicted in one or more images captured by an image captured device of vehicle  7902   a ) may be may be sent to a server (e.g., server  1230 ) for analysis. 
     The server side may analyze the received information (e.g., using automated image analysis processes) to determine whether any updates to sparse data model  800  are warranted based on whether or not the adjustment was due to a transient condition. Where the adjustment was not due to the existence of a transient condition, the road model may be updated. For example, where an experienced condition is determined to be one likely to persist beyond a predetermined time threshold (e.g., a few hours, a day, or a week or more) updates may be made to the model. In some embodiments, the threshold for determining a transient condition may be dependent on a geographic region in which the condition is determined to occur, on an average number of vehicles that travel the road segment in which the condition was encountered, or any other suitable criteria. For example, in geographic regions, such as rural regions, that include fewer vehicles likely to encounter a road-related condition, a time threshold for making the transient or not transient determination may be longer that another geographic region (e.g., an urban environment) that includes more vehicles likely to encounter the road-related condition over a particular time period. That is, as the average number of vehicles traveling a road segment increases, the time threshold for making the transient determination may be lower. Such an approach may reduce the number of cars traveling in an urban environment that will need to rely upon their internal systems (camera, sensors, processor, etc.) to recognize a road condition that warrants a navigational response different from one expected based on sparse model  800 . At the same time, a longer transient time threshold in lower trafficked areas may reduce the likelihood that the model is changed to account for an experienced road condition and, a short time later (e.g., within hours, a day, etc.) needs to be changed back to its original state, for example, after the experience road condition no longer exists. 
     Conversely, where a navigational adjustment is determined to be in response to a transient condition, the server may elect to not make any updates to the road model. For example, where either the system or user intervenes to navigate the vehicle  7902   a  into lane  7920  to avoid vehicles  7902   c  parked on the shoulder yet abutting into lane  7910  ( FIG. 84A ), wherein the system or user navigates the host vehicle to avoid an intervening car  7902   d  ( FIG. 84B ), wherein the system or user navigates the host vehicle to avoid a temporary barrier  8402  (such as a fallen tree, as shown in  FIG. 84C ), or where the system or user navigates the host vehicle to avoid markers  8200   d  designating temporary roadwork ( FIG. 84D ), where the system or user navigates the host vehicle to avoid a pothole  8502  present in the roadway ( FIG. 85A ), where the system or user navigates the host vehicle to avoid a pedestrian  8504  or pedestrian in the roadway ( FIG. 85B ), the server may determine in each case that the experienced condition constitutes a transient condition not warranting an update to sparse data model  800 . 
     In some cases, and as described above, certain road conditions may be classified as transient based on a determination of a probable time of their existence (less than a few hours, a day, a week, etc.). In other cases, a determination of whether a certain road condition is a transient one may be based on factors other than or in addition to time. For example, in the case of a pothole captured in one or more images, the server (or the processing unit associated with a host vehicle) may determine a depth of the pothole, which may aid in determining whether the pothole represents a transient condition and, therefore, whether sparse data model  800  should be updated in view of the pothole. If the pothole  8502  is determined to have a depth that could result in potential damage to the host vehicle if driven through (e.g., a depth on the order of greater than 3 cm, 5 cm, 10 cm or more), then the pothole may be categorized as non-transient. Similarly, if the pothole  8502  is located in a geographic region in which road repair is known to be somewhat slow (e.g., requiring more than a day to repair, a week to repair, or longer), then a pothole may be categorized as non-transient. 
     Determination of whether a particular road condition constitutes a transient condition may be fully automated and performed by one or more server-based systems. For example, in some embodiments, the one or more server based systems may employ automated image analysis techniques based on one or more images captured by cameras onboard a host vehicle. In some embodiments, the image analysis techniques may include machine learning systems trained to recognize certain shapes, road features, and/or objects. For example, the server may be trained to recognized in an image or image stream the presence of a concrete barrier (possibly indicating the presence of a non-transient construction or lane separation condition), a pothole in the surface of the road (a possible transient or non-transient condition depending on the size, depth, etc.), a road edge intersecting with an expected path of travel (potentially indicating a non-transient lane shift or new traffic pattern), a parked car (a potentially transient condition), an animal shape in the road (a potentially transient condition), or any other relevant shapes, objects, or road features. 
     The image analysis techniques employed by the server may also include a text recognition component to determine a meaning associated with text present in an image. For example, where text appears in one or more uploaded images from an environment of a host vehicle, the server may determine whether text exists in the images. If text exists, the server may use techniques such as optical character recognition to assist in determining whether the text may relate to a reason that a system or user of a host vehicle caused a navigational maneuver differing from that expected based on sparse model  800 . For example, where a sign is identified in an image, and the sign is determined to include the text “NEW TRAFFIC PATTERN AHEAD,” the text may assist the server in determining that the experienced condition had a non-transient nature. Similarly, signs such as “ROAD CLOSED AHEAD” or “BRIDGE OUT” may also help indicate the presence of a non-transient condition for which an update to sparse road model  800  may be justified. 
     The server-based system may also be configured to take into account other information when determining whether an experienced road condition is transient. For example, the server may determine an average number of vehicles that travel a road segment over a particular amount of time. Such information may be helpful in determining the number of vehicles a temporary condition is likely to affect over an amount of time that the condition is expected to persist. Higher numbers of vehicles impacted by the condition may suggest a determination that the sparse data model  800  should be updated. 
     In some embodiments, determination of whether a particular road condition constitutes a transient condition may include at least some level of human assistance. For example, in addition to the automated features described above, a human operator may also be involved in reviewing information uploaded from one or more vehicles and/or determining whether sparse data model  800  should be updated in view of the received information. 
       FIG. 86  illustrates an example flowchart representing a method for an adaptive road model manager consistent with disclosed embodiments. In particular,  FIG. 86  illustrates a process  8600  for an adaptive road model manager consistent with disclosed embodiments. Steps of process  8600  may be performed by a server (e.g., server  1230 ), which may receive data from a plurality of autonomous vehicles over one or more networks (e.g., cellular and/or the Internet, etc.). 
     At step  8610 , the server may receive from each of a plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver. The navigational situation information may result from system or user invention overriding the road model. The navigational situation information may include at least one image or a video representing an environment of vehicle  7902 . In some embodiments, the navigational situation information may further include a location of vehicle  7902  (e.g., as determined by position sensor  130  and/or based on a distance of vehicle  7902  to a recognized landmark). 
     At step  8612 , the server may analyze the navigational situation information. For example, the server side may analyze the received information (e.g., using automated image analysis processes) to determine what is depicted in the at least one image or video representing an environment of vehicle  7902 . This analysis may include identification of the existence of, for example, a parked car, an intervening car, a temporary barrier, such as a fallen tree directly in front of a vehicle, roadwork, a low light condition, a glare condition, a pothole, an animal, or a pedestrian. 
     At step  8614 , the server may determine, based on the analysis of the navigational situation information, whether the adjustment to the determined maneuver was due to a transient condition. For example, A transient condition may include where a second vehicle is parked directly in front of a vehicle, a vehicle intervenes directly in front of vehicle, barrier, such as a fallen tree lies directly in front of a vehicle, a low light condition, a glare condition, a pothole (e.g., one of a minimal depth), an animal, or a pedestrian. 
     At step  8616 , process  8600  may include the server updating the predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition. For example, a condition that may be non-transient may include a substantial pothole, long-term and/or extensive roadwork, etc. This update may include an update to the three-dimensional spline representing a predetermined path of travel along at least one road segment. 
     Road Model Management Based on Selective Feedback 
     In some embodiments, the disclosed systems and methods may manage a road model based on selective feedback received from one or more vehicles. As discussed in earlier sections, the road model may include a target trajectory (e.g., a three-dimensional spline representing a predetermined path of travel along a road segment). Consistent with disclosed embodiments, a server (e.g., server  1230 ) may selectively receive road environment information from autonomous vehicles in order to update the road model. As used herein, road environment information may include any information related to an observable or measurable condition associated with a road or a road segment. The server may selectively receive the road environment information based on a variety of criteria. Relative to the disclosed embodiments, selectively receiving information may refer to any ability of a server based system to limit data transmissions sent from one or more autonomous vehicles to the server. Such limitations placed on data transmissions from the one or more autonomous vehicles may be made based any suitable criteria. 
     For example, in some embodiments, the server may limit a frequency at which road environment information is uploaded to the server from a particular vehicle, from a group of vehicles, and/or from vehicles traveling within a particular geographic region. Such limitations may be placed based on a determined model confidence level associated with a particular geographic region. In some embodiments, the server may limit data transmissions from autonomous vehicles to only those transmissions including information suggesting a potential discrepancy with respect to at least one aspect of the road model (such information, for example, may be determined as prompting one or more updates to the model). The server may determine whether one or more updates to the road model are required based on the road environment information selectively received from the autonomous vehicles and may update the road model to include the one or more updates. Examples of a server selectively receiving road environment information from autonomous vehicles are discussed below. 
       FIG. 87A  illustrates a plan view of a vehicle traveling on an interstate roadway consistent with the disclosed embodiments. As shown, vehicle  7902  is traveling along a predetermined path of travel  8700  (e.g., a target trajectory according to a road model) associated with interstate roadway  7900 . As shown, roadway  7900  may be subdivided into lanes, such as lanes  7910  and  7920 . The server may selectively receive road environment information based on navigation by vehicle  7902  through a road environment, such as roadway  7900 . For example, the road environment information may include one or more images captured by an image capture device of vehicle  7902 ; location information representing a position of vehicle  7902  determined by, for example, using position sensor  130  and/or based on a position of vehicle  7902  relative to a recognized landmark; outputs from one or more sensors associate with vehicle  7902 , etc. Based upon the road environment information, the server may determine whether updates to the road model are required. 
     In the example shown in  FIG. 87A , a single particular vehicle  7902  is shown traveling along an interstate roadway  7900  and following a target trajectory  8700 .  FIG. 87B  illustrates a plan view of a group of vehicles  7902   e,    7902   f,    7902   g,  and  7902   h  traveling along a city roadway  7900  and following target trajectories  8700   a  and  8700   b  that may be associated with lanes  7910  and  7920  of roadway  7900 , for example.  FIG. 87C  illustrates a plan view of a vehicle  7902   i  traveling within a rural geographic region  8722  on roadway  7900 .  FIG. 87D  illustrates a vehicle  7902  traveling on a roadway  7900  including a newly modified traffic pattern. For example, where once lane  7910  may have extended forward of vehicle  7902 , a new traffic pattern may exist where lane  7910  now comes to an end forward of vehicle  7902 . 
     Information relating to the navigation of vehicle  7902  in any of these situations, among others, may be collected and uploaded to one or more server based systems that maintain sparse data map  800 . Based on the received information, the server may analyze whether one or more updates are needed to sparse data map  800  and, if an update is determined to be justified, then the server may make the update to sparse data map  800 . In some embodiments, the analysis and updating may be performed automatically by the server via automated image analysis of images captured by cameras aboard vehicle  7910 , automated review of sensor and position information, automated cross-correlation of information received from multiple autonomous vehicles, etc. In some embodiments, an operator associated with the server-based system may assist in review of the information received from the autonomous vehicles and determination of whether updates to sparse data model  800  are needed based on the received information. 
     In some embodiments, the server may be configured to receive navigational information from all available autonomous vehicles. Further, this information may be uploaded to the server based on a predetermined protocol. For example, the information may be uploaded across a streaming data feed. Additionally or alternatively, the information may be uploaded to the server at a predetermined periodic rate (e.g., several times per second, once per second, once per minute, once every several minutes, once per hour, or any other suitable time interval). The information may also be uploaded to the server based on aspects of the vehicle&#39;s navigation. For example, navigational information may be uploaded from a vehicle to the server as the vehicle moves from one road segment to another or as the vehicle moves from one local map associated with sparse data map  800  to another. 
     In some embodiments, the server may be configured to selectively control the receipt of navigational information from one or more autonomous vehicle. That is, rather than receiving all available navigational information from all available autonomous vehicles, the server may restrict the amount of information it receives from one or more available autonomous vehicles. In this way, the server may reduce the amount of bandwidth needed for communicating with available autonomous vehicles. Such selective control of information flow from the autonomous vehicles and the server may also reduce an amount of processing resources required to process the communications incoming from the autonomous vehicles. 
     The selective control of information flow between the autonomous vehicles and the server may be based on any suitable criteria. In some embodiments, the selectivity may be based on the type of road that a vehicle is traversing. With reference to the example shown in  FIG. 87A , vehicle  7902  is traversing an interstate, which may be a well-traveled road. In such situations, the server may have accumulated a significant amount of navigational information relating to the interstate road, its various lanes, the landmarks associated with the road, etc. In such circumstances, continuing to receive full information uploads from every vehicle that travels along the interstate roadway may not contribute to significant or further refinements of the road model represented in sparse data map  800 . Therefore, the server may limit, or an autonomous vehicle traveling along a certain type of road or a particular road segment may limit, the amount or type of information uploaded to the server. 
     In some embodiments, the server may forego automatic information uploads altogether from vehicles traveling along a particular interstate roadway, a heavily traveled urban road, or any other road where sparse data model  800  is determined to require no additional refinements. Instead, in some embodiments, the server may selectively acquire data from vehicles traveling along such roads as a means for periodically confirming that sparse data map  800  remains valid along selected roadways. For example, the server may interrogate one or more vehicles determined to be traveling along an interstate, heavily traveled road segment, etc. to collect navigational information from the interrogated vehicle. This information may include information relating to a reconstructed trajectory of the vehicle along the roadway, a position of the vehicle on the roadway, sensor information from the vehicle, captured images from cameras onboard the vehicle, etc. Using this technique, the server may periodically monitor the state of a roadway and determine whether updates are needed to sparse data model  800  without unnecessary usage of data transmission and/or data processing resources. 
     In some embodiments, the server may also selectively control data flow from an autonomous vehicle based on the number of cars determined to be traveling within a group along a roadway. For example, where a group of autonomous vehicles (e.g., two or more vehicles) is determined to be traveling within a certain proximity of one another (e.g., within 100 meters, 1 km, or any other suitable proximity envelope), information upload may be restricted from any of the members of the group. For example, the server may restrict information transfer to only one member of the group, any subset of members of the group, one member of the group from each lane of the road, etc. 
     In some embodiments, the server may also selectively control data flow from an autonomous vehicle based on a geographic region. For example, some geographic regions may include road segments for which sparse data model  800  already includes refined target trajectories, landmark representations, landmark positions, etc. For example, in certain geographic regions (e.g., urban environments, heavily traveled roadways, etc.), sparse data model  800  may be generated based upon multiple traversals of various road segments by vehicles in a data collection mode. Each traversal may result in additional data relevant to road segments in a geographic region from which sparse data model  800  may be refined. In some cases, sparse data map  800  for certain geographic regions may be based upon 100, 1000, 10000 or more prior traversals of various road segments. In those regions, additional information received from one or more autonomous vehicles may not serve as a basis for further, significant refinements of sparse data model. Thus, the server may restrict uploads from vehicles traveling in certain geographic regions. For example, in some cases, the server may preclude all automatic transmissions of road data from vehicles traveling in selected geographic regions. In other cases, the server may enable transmission of data from only a portion of vehicles traveling in a certain geographic region (e.g., 1 of 2 vehicles, 1 of 5, 1 of 100, etc.). In other cases, the server may receive transmissions from only those vehicles in a geographic location that the server identifies and queries for updated road information. The server can use information received from any portion of the vehicles from a certain geographic region to verify and/or update any aspect of sparse data model  800 . 
     In some embodiments, the server may also selectively control data flow from an autonomous vehicle based on a confidence level assigned to a particular local map, road segment, geographic region, etc. For example, like the geographic region example, certain road segments, local maps, and/or geographic regions may be associated with a confidence level indicative of, for example, a level of refinement of sparse data map  800  in those areas. The server may restrict transmission of road information from vehicles traveling on any roads, local map areas, or geographic regions associated with a confidence level above a predetermined threshold. For example, in some cases, the server may preclude all automatic transmissions of road data from vehicles traveling in regions with a confidence level above a predetermined threshold. In other cases, the server may enable transmission of data from only a portion of vehicles traveling in those regions (e.g., 1 of 2 vehicles, 1 of 5, 1 of 100, etc.). In other cases, the server may receive transmissions from only those vehicles in a high-confidence area (one including a confidence level above a predetermined threshold) that the server identifies and queries for updated road information. The server can use information received from any portion of the vehicles from a high-confidence level region to verify and/or update any aspect of sparse data model  800 . 
     In some embodiments, the server may also selectively control data flow from an autonomous vehicle based on the type of information included within the navigational information to be uploaded by a particular autonomous vehicle. For example, in many cases, the road information uploaded to the server from various host vehicles may not significantly impact sparse data model  800 . For example, in high-confidence level geographic areas or road segments etc., additional road information from traversing vehicles may be useful for verifying the continued accuracy of sparse data model  800 , but such information may not offer a potential for additional significant refinements to sparse data model  800 . Thus, continued transmission of information that verifies sparse data model  800 , but does not offer a potential for significant further refinement of sparse data model  800  may consume data transmission and processing resources without a potential for significant benefit. 
     In such cases, it may be desirable for the server to limit data transmissions from vehicles. Instead of receiving data transmissions automatically from all (or even a part of) available vehicles, the server may restrict data transmissions from vehicles to only those experiencing situations that may impact sparse road model  800 . For example, where a vehicle traversing a road segment experiences a situation that requires a navigational response that departs from one anticipated by the sparse data model  800  (e.g., where the vehicle must travel a path different from a target trajectory for a road segment), then the processing unit  110  may determine that such a departure has occurred and may relay that information to the server. In response, the server may query the vehicle for information relating to the navigational departure so that the server can determine whether any updates are needed to sparse data model  800 . In other words, the server may elect to receive road information from vehicles only where the information suggests that a change may be needed to sparse data model  800 . 
       FIG. 88  illustrates an example flowchart representing a method for road model management based on selective feedback consistent with the disclosed embodiments. Steps of process  8800  may be performed by a server (e.g., server  1230 ). As discussed below, process  8800  may involve selectively receiving feedback to potentially update the road model based upon road environment information from autonomous vehicles. 
     At step  8810 , the server may selectively receive road environment information based on navigation from a plurality of autonomous vehicles through their respective road environments. For example, the server may selectively apply a limitation on a frequency of information transmissions received from a particular vehicle, from a group of vehicles, from vehicles traveling within a particular geographic region, or from vehicles based on a determined model confidence level associated with a particular geographic region. Further, in some embodiments, the server may selectively limit data transmissions from vehicles only to those transmissions that reflect a potential discrepancy with respect to at least one aspect of a predetermined road model. 
     At step  8812 , the server may determine whether one or more updates to the road model are required based on the road environment information. If the server determines that updates to the road model are justified based on information selectively received from one or more autonomous vehicles, those updates may be made at step  8814 . 
     The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray, or other optical drive media. 
     Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. The various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets. 
     Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.