Patent Publication Number: US-2017359561-A1

Title: Disparity mapping for an autonomous vehicle

Description:
BACKGROUND 
     Autonomous vehicles (AVs) may require continuous sensor data processing in order to operate through road traffic on public roads in order to match or even surpass human capabilities. AVs can be equipped with many kinds of sensors, including stereoscopic cameras, but processing images from a stereoscopic camera in real-time with enough fidelity to properly identify and classify obstacles is a challenge. 
     In stereo vision, images are captured from a pair of cameras or lenses of a camera that are slightly displaced relative to each other. This positional difference is known as horizontal disparity and allows a stereo camera to perceive and calculate depth, or the distance from the camera to objects in a scene. At present, stereoscopic imaging is mostly fulfilled by utilizing a parallax effect. By providing a left image for a left eye and a right image for a right eye, it is possible to convey a 3D impression to a viewer when the viewer is watching the images at an appropriate viewing angle. A two-view stereoscopic video is a video generated by utilizing such an effect and each frame of the video includes an image for a left eye and another image for a right eye. The depth information of objects in the frame can be obtained by processing the two-view stereoscopic video. The depth information for all pixels of the image makes up a disparity map. 
     Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene. The optical flow methods try to calculate the motion, for each pixel or voxel position, between two image frames which are taken at separate times. An optical flow sensor is a vision sensor capable of measuring optical flow or visual motion and outputting a measurement based on optical flow. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosure herein is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements, and in which: 
         FIG. 1  illustrates an example control system for operating an autonomous vehicle; 
         FIG. 2  illustrates an example autonomous vehicle including an improved disparity mapping and object classification system; 
         FIG. 3  illustrates an example method of object classification in accordance with one or more embodiments; 
         FIG. 4  illustrates an example method of disparity mapping in accordance with one or more embodiments; and 
         FIG. 5  is a block diagram illustrating a computer system upon which examples described herein may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     An improved disparity mapping system is disclosed that enables an autonomous vehicle (AV) to efficiently calculate disparity maps from images taken by a stereo camera mounted on the AV. The disparity mapping system utilizes mapping resource data and previously recorded sub-maps that contain surface data for a given region and compares this sub-map data with the images taken in order to improve disparity map calculations, both in terms of accuracy and speed. A classifier can then use these disparity maps along with optical flow images to classify objects and assist the AV in maneuvering through road traffic to a particular destination. For example, the disparity mapping system can utilize a sub-map that includes recorded 3D LIDAR data and 3D stereo data of the current route traveled by the AV. The system can continuously compare real-time sensor data to the pre-recorded data of the current sub-map to help classify potential hazards, such as pedestrians, other vehicles, bicyclists, etc. Accordingly, an AV control system can use these classifications to better avoid collisions with dangerous hazards. 
     Relative depth information for objects in a scene can be obtained in the form of a disparity map, which encodes the difference in coordinates of corresponding pixels between two images taken from a stereoscopic camera. The values in a disparity map are inversely proportional to the scene depth at the corresponding pixel location. The two images are captured from a pair of lenses that are slightly displaced relative to each other. This positional difference is known as horizontal disparity and allows a stereo camera to perceive and calculate depth, or the distance from the camera to objects in a scene. The depth information for all pixels in the images is obtained by processing the two images to construct a disparity map. 
     Processing the images from the stereo camera involves finding a set of points in one image which can be identified as the same set of points in the other image. To do this, points or features in one image are matched with the corresponding points or features in the other image to create a disparity map. This processing is known as the correspondence problem, and solving it can be very time-consuming and processor intensive for a computing device. When processing stereoscopic images in real-time, the calculation load is heavy since each individual frame has to be computed to obtain a corresponding disparity map. For a two-view stereoscopic camera having a left image and a right image of with sufficient resolution to identify objects in a scene, the full computation of a disparity map can take a few minutes using conventional algorithms. 
     Other algorithms for computing disparity maps can perform the calculations in less time, but at a cost of accuracy. Considering the need to properly identify depth and classify objects in an AV, a loss of precision in computing the disparity maps can be an unacceptable trade-off. Moreover, even the fastest conventional algorithms take several seconds to compute the disparity map for a scene, which is too slow for real-time processing in an AV travelling down a street. Therefore, how to improve the efficiency of the disparity map calculation while maintaining the accuracy of the disparity map is an important issue in computer vision. 
     In some examples, a set of imaging devices such as a stereoscopic camera acquires a first image and a second image of a scene. A system generates baseline disparity data from a location and orientation of the stereoscopic camera and three-dimensional environment data for the scene. Using the first image, second image, and baseline disparity data, the system can then generate a disparity map for the scene. 
     According to one aspect, the system compares, for each pixel in the first image, the pixel to the baseline disparity data to determine a likely location in the second image for a matching pixel that corresponds to the pixel in the first image, wherein the pixel in the first image and the matching pixel in the second image correspond to an object in the scene. When generating the disparity map, the likely locations in the second image can be used for at least some of the pixels in the first image to reduce a search space when locating the matching pixels in the second image. 
     In some aspects, generating the baseline disparity data uses a ray casting algorithm to render the 3D environment data into a 2D image. 
     In some aspects, the 3D environment data is ground-based data corresponding to a location of the stereoscopic camera that is compiled from a fleet of autonomous vehicles. 
     One or more examples described herein provide that methods, techniques, and actions performed by a computing device are performed programmatically, or as a computer-implemented method. Programmatically, as used herein, means through the use of code or computer-executable instructions. These instructions can be stored in one or more memory resources of the computing device. A programmatically performed step may or may not be automatic. 
     One or more examples described herein can be implemented using programmatic modules, engines, or components. A programmatic module, engine, or component can include a program, a sub-routine, a portion of a program, or a software component or a hardware component capable of performing one or more stated tasks or functions. As used herein, a module or component can exist on a hardware component independently of other modules or components. Alternatively, a module or component can be a shared element or process of other modules, programs or machines. 
     Some examples described herein can generally require the use of computing devices, including processing and memory resources. For example, one or more examples described herein may be implemented, in whole or in part, on computing devices such as servers, desktop computers, cellular or smartphones, personal digital assistants (e.g., PDAs), laptop computers, printers, digital picture frames, network equipment (e.g., routers) and tablet devices. Memory, processing, and network resources may all be used in connection with the establishment, use, or performance of any example described herein (including with the performance of any method or with the implementation of any system). 
     Furthermore, one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors. These instructions may be carried on a computer-readable medium. Machines shown or described with figures below provide examples of processing resources and computer-readable mediums on which instructions for implementing examples disclosed herein can be carried and/or executed. In particular, the numerous machines shown with examples of the invention include processors and various forms of memory for holding data and instructions. Examples of computer-readable mediums include permanent memory storage devices, such as hard drives on personal computers or servers. Other examples of computer storage mediums include portable storage units, such as CD or DVD units, flash memory (such as carried on smartphones, multifunctional devices or tablets), and magnetic memory. Computers, terminals, network enabled devices (e.g., mobile devices, such as cell phones) are all examples of machines and devices that utilize processors, memory, and instructions stored on computer-readable mediums. Additionally, examples may be implemented in the form of computer-programs, or a computer usable carrier medium capable of carrying such a program. 
     Numerous examples are referenced herein in context of an autonomous vehicle (AV). An AV refers to any vehicle which is operated in a state of automation with respect to steering and propulsion. Different levels of autonomy may exist with respect to AVs. For example, some vehicles may enable automation in limited scenarios, such as on highways, provided that drivers are present in the vehicle. More advanced AVs drive without any human assistance from within or external to the vehicle. Such vehicles often are required to make advance determinations regarding how the vehicle is behave given challenging surroundings of the vehicle environment. 
     System Description 
       FIG. 1  illustrates an example control system for operating an autonomous vehicle. In an example of  FIG. 1 , a control system  100  can be used to autonomously operate an AV  10  in a given geographic region for a variety of purposes, including transport services (e.g., transport of humans, delivery services, etc.). In examples described, an autonomously driven vehicle can operate without human control. For example, in the context of automobiles, an autonomously driven vehicle can steer, accelerate, shift, brake, and operate lighting components. Some variations also recognize that an autonomous-capable vehicle can be operated either autonomously or manually. 
     In one implementation, the control system  100  can utilize specific sensor resources in order to intelligently operate the vehicle  10  in most common driving situations. For example, the control system  100  can operate the vehicle  10  by autonomously steering, accelerating, and braking the vehicle  10  as the vehicle progresses to a destination. The control system  100  can perform vehicle control actions (e.g., braking, steering, accelerating) and route planning using sensor information, as well as other inputs (e.g., transmissions from remote or local human operators, network communication from other vehicles, etc.). 
     In an example of  FIG. 1 , the control system  100  includes a computer or processing system which operates to process sensor data  99  that is obtained on the vehicle with respect to a road segment upon which the vehicle  10  operates. The sensor data  99  can be used to determine actions which are to be performed by the vehicle  10  in order for the vehicle  10  to continue on a route to a destination. In some variations, the control system  100  can include other functionality, such as wireless communication capabilities, to send and/or receive wireless communications with one or more remote sources. In controlling the vehicle  10 , the control system  100  can issue instructions and data, shown as commands  85 , which programmatically controls various electromechanical interfaces of the vehicle  10 . The commands  85  can serve to control operational aspects of the vehicle  10 , including propulsion, braking, steering, and auxiliary behavior (e.g., turning lights on). 
     The AV  10  can be equipped with multiple types of sensors  101  and  103 , which combine to provide a computerized perception of the space and environment surrounding the vehicle  10 . Likewise, the control system  100  can operate within the AV  10  to receive sensor data  99  from the collection of sensors  101  and  103 , and to control various electromechanical interfaces for operating the vehicle on roadways. 
     In more detail, the sensors  101  and  103  operate to collectively obtain a complete sensor view of the vehicle  10 , and further to obtain situational information proximate to the vehicle  10 , including any potential hazards in a forward operational direction of the vehicle  10 . By way of example, the sensors can include proximity or touch sensors, remote detection sensors such as provided by radar or LIDAR, a stereo camera  105  (stereoscopic pairs of cameras or depth perception cameras), and/or sonar sensors. 
     Each of the sensors  101  and  103  and stereo camera  105  can communicate with the control system  100  utilizing a corresponding sensor interface  110 ,  112  or camera interface  114 . Each of the interfaces  110 ,  112 ,  114  can include, for example, hardware and/or other logical components which are coupled or otherwise provided with the respective sensor. For example, camera interface  114  can connect to a video camera and/or stereoscopic camera  105  which continually generates image data of an environment of the vehicle  10 . The stereo camera  105  can include a pair of imagers, each of which is mounted to a rigid housing structure that maintains the alignment of the imagers on a common plane when the vehicle is in motion. As an addition or alternative, the interfaces  110 ,  112 ,  114  can include a dedicated processing resource, such as provided with a field programmable gate array (“FPGA”) which can, for example, receive and/or process raw image data from the camera sensor. 
     In some examples, the interfaces  110 ,  112 ,  114  can include logic, such as provided with hardware and/or programming, to process sensor data  99  from a respective sensor  101  or  103 . The processed sensor data  99  can be outputted as sensor data  111 . Camera interface  114  can process raw image data from stereo camera  105  into images  113  for the control system  100 . As an addition or variation, the control system  100  can also include logic for processing raw or pre-processed sensor data  99  and images  113 . 
     According to one implementation, the vehicle interface subsystem  90  can include or control multiple interfaces to control mechanisms of the vehicle  10 . The vehicle interface subsystem  90  can include a propulsion interface  92  to electrically (or through programming) control a propulsion component (e.g., an accelerator pedal), a steering interface  94  for a steering mechanism, a braking interface  96  for a braking component, and a lighting/auxiliary interface  98  for exterior lights of the vehicle. The vehicle interface subsystem  90  and/or the control system  100  can include one or more controllers  84  which can receive one or more commands  85  from the control system  100 . The commands  85  can include route information  87  and one or more operational parameters  89  which specify an operational state of the vehicle  10  (e.g., desired speed and pose, acceleration, etc.). 
     The controller(s)  84  can generate control signals  119  in response to receiving the commands  85  for one or more of the vehicle interfaces  92 ,  94 ,  96 ,  98 . The controllers  84  can use the commands  85  as input to control propulsion, steering, braking, and/or other vehicle behavior while the AV  10  follows a current route. Thus, while the vehicle  10  is actively driven along the current route, the controller(s)  84  can continuously adjust and alter the movement of the vehicle  10  in response to receiving a corresponding set of commands  85  from the control system  100 . Absent events or conditions which affect the confidence of the vehicle  10  in safely progressing along the route, the control system  100  can generate additional commands  85  from which the controller(s)  84  can generate various vehicle control signals  119  for the different interfaces of the vehicle interface subsystem  90 . 
     According to examples, the commands  85  can specify actions to be performed by the vehicle  10 . The actions can correlate to one or multiple vehicle control mechanisms (e.g., steering mechanism, brakes, etc.). The commands  85  can specify the actions, along with attributes such as magnitude, duration, directionality, or other operational characteristic of the vehicle  10 . By way of example, the commands  85  generated from the control system  100  can specify a relative location of a road segment which the AV  10  is to occupy while in motion (e.g., change lanes, move into a center divider or towards shoulder, turn vehicle, etc.). As other examples, the commands  85  can specify a speed, a change in acceleration (or deceleration) from braking or accelerating, a turning action, or a state change of exterior lighting or other components. The controllers  84  can translate the commands  85  into control signals  119  for a corresponding interface of the vehicle interface subsystem  90 . The control signals  119  can take the form of electrical signals which correlate to the specified vehicle action by virtue of electrical characteristics that have attributes for magnitude, duration, frequency or pulse, or other electrical characteristics. 
     In an example of  FIG. 1 , the control system  100  can include a route planner  122 , optical flow unit  121 , disparity mapper  126 , classifier  127 , event logic  124 , and a vehicle control  128 . The vehicle control  128  represents logic that converts alerts of event logic  124  (“event alert  135 ”) into commands  85  that specify a set of vehicle actions. 
     Additionally, the route planner  122  can select one or more route segments that collectively form a path of travel for the AV  10  when the vehicle  10  is on a current trip (e.g., servicing a pick-up request). In one implementation, the route planner  122  can specify route segments  131  of a planned vehicle path which defines turn by turn directions for the vehicle  10  at any given time during the trip. The route planner  122  may utilize the sensor interface  110  to receive GPS information as sensor data  111 . The vehicle control  128  can process route updates from the route planner  122  as commands  85  to progress along a path or route using default driving rules and actions (e.g., moderate steering and speed). 
     According to examples described herein, the control system  100  includes an optical flow unit  121  and disparity mapper  126  to monitor the situational environment of the AV  10  continuously in order to dynamically calculate disparity maps and optical flow images as the AV  10  travels along a current route. The external entity can be a pedestrian or group of pedestrians, a human-driven vehicle, a bicyclist, and the like. 
     The sensor data  111  captured by the sensors  101  and  103  and images  113  from the camera interface  114  can be processed by an on-board optical flow unit  121  and disparity mapper  126 . Optical flow unit  121  and disparity mapper  126  can utilize mapping resource data and previously recorded sub-maps that contain surface data for a given region. Disparity mapper  126  can compare this sub-map data with the images  113  taken from stereo camera  105  in order to improve disparity map calculations, both in terms of accuracy and speed. Classifier  127  can then use these maps and optical flow images to create object classifications  133  to assist the AV  10  in maneuvering through road traffic to a particular destination. For example, the disparity mapper  126  can utilize a current sub-map that includes recorded 3D LIDAR data and 3D stereo data of the current route traveled by the AV  10 . The disparity mapper  126  can continuously compare the sensor data  111  to the 3D LIDAR data and stereo data of the current sub-map to help classifier  127  identify potential hazards, such as pedestrians, other vehicles, bicyclists, etc. Accordingly, classifier  127  can generate object classifications  133  for event logic  124 . 
     In certain implementations, the event logic  124  can refer to the object classifications  133  in determining whether to trigger a response to a detected event. A detected event can correspond to a roadway condition or obstacle which, when detected, poses a potential hazard or threat of collision to the vehicle  10 . By way of example, a detected event can include an object in the road segment, heavy traffic ahead, and/or wetness or other environment conditions on the road segment. The event logic  124  can use sensor data  111  and images  113  from cameras, LIDAR, radar, sonar, or various other image or sensor component sets in order to detect the presence of such events as described. For example, the event logic  124  can detect potholes, debris, objects projected to be on a collision trajectory, and the like. Thus, the event logic  124  can detect events which enable the control system  100  to make evasive actions or plan for any potential threats. 
     When events are detected, the event logic  124  can signal an event alert  135  that classifies the event and indicates the type of avoidance action to be performed. Additionally, the control system  100  can determine whether an event corresponds to a potential incident with a human driven vehicle, a pedestrian, or other human entity external to the AV  10 . An event can be scored or classified between a range of likely harmless (e.g., small debris in roadway) to very harmful (e.g., vehicle crash may be imminent) from the sensor data  111  and object classifications  133 . In turn, the vehicle control  128  can determine a response based on the score or classification. Such response can correspond to an event avoidance action  145 , or an action that the vehicle  10  can perform to maneuver the vehicle  10  based on the detected event and its score or classification. By way of example, the vehicle response can include a slight or sharp vehicle maneuvering for avoidance using a steering control mechanism and/or braking component. The event avoidance action  145  can be signaled through the commands  85  for controllers  84  of the vehicle interface subsystem  90 . 
     When an anticipated dynamic object with a particular classification moves into a position of likely collision or interference, some examples provide that event logic  124  can signal an event alert  135  to cause the vehicle control  128  to generate commands  85  that correspond to an event avoidance action  145 . For example, in the event of a bicycle crash in which the bicycle (or bicyclist) falls into the path of the vehicle  10 , event logic  124  can signal an event alert  135  to avoid the collision. The event alert  135  can indicate (i) a classification of the event (e.g., “serious” and/or “immediate”), (ii) information about the event, such as the type of object that generated the event alert  135 , and/or information indicating a type of action the vehicle  10  should take (e.g., location of object relative to path of vehicle, size or type of object, etc.). 
       FIG. 2  illustrates an example autonomous vehicle including an improved disparity mapping and object classification system. The AV  200  shown in  FIG. 2  can include some or all aspects and functionality of the autonomous vehicle  10  described with respect to  FIG. 1 . Referring to  FIG. 2 , the AV  200  can include a sensor array  205  that can provide sensor data  207  to an on-board data processing system  210 . As described herein, the sensor array  205  can include any number of active or passive sensors that continuously detect a situational environment of the AV  200 . For example, the sensor array  205  can include a number of camera sensors (e.g., stereo camera  206 ), LIDAR sensor(s), proximity sensors, radar, and the like. The data processing system  210  can utilize the sensor data  207  and images  208  to detect the situational conditions of the AV  200  as the AV  200  travels along a current route. For example, the data processing system  210  can identify potential obstacles or road hazards, such as pedestrians, bicyclists, objects on the road, road cones, road signs, animals, etc., which classifier  235  can classify in order to enable an AV control system  220  to react accordingly. 
     The AV  200  can further include a database  230  that includes sub-maps  231  for the given region in which the AV  200  operates. The sub-maps  231  can comprise detailed road data previously recorded by a recording vehicle using sensor equipment, such as LIDAR, stereo camera, and/or radar equipment. In some aspects, several or all AVs in the fleet can include this sensor equipment to record updated sub-maps  231  along traveled routes and submit the updated sub-maps  231  to the backend system  290 , which can transmit the updated sub-maps  231  to the other AVs in the fleet for storage. Accordingly, the sub-maps  231  can comprise ground-based, three-dimensional (3D) environment data along various routes throughout the given region (e.g., a city). 
     In many aspects, the on-board data processing system  210  can provide continuous processed data  214  to the AV control system  220  to respond to point-to-point activity in the AV&#39;s  200  surroundings. The processed data  214  can comprise comparisons between the actual sensor data  207 —which represents an operational environment of the AV  200 , and which is continuously collected by the sensor array  205 —and the stored sub-maps  231  (e.g., LIDAR-based sub-maps). In certain examples, the data processing system  210  is programmed with machine learning capabilities to enable the AV  200  to identify and respond to conditions, events, or potential hazards. In variations, the on-board data processing system  210  can continuously compare sensor data  207  to stored sub-maps  231  in order to perform a localization to continuously determine a location and orientation of the AV  200  within the given region. Localization of the AV  200  is necessary in order to make the AV  200  self-aware of its instant location and orientation in comparison to the stored sub-maps  231  in order to maneuver the AV  200  on surface streets through traffic and identify and respond to potential hazards, such as pedestrians, or local conditions, such as weather or traffic. 
     The data processing system  210  can compare the sensor data  207  from the sensor array  205  with a current sub-map  238  from the sub-maps  231  to identify obstacles and potential road hazards in real time. In some aspects, a disparity mapper  211  and optical flow unit  212 , which can be part of the data processing system  210 , process the sensor data  207 , images  208  from the stereo camera  206 , and the current sub-map  238  to create image maps  218  (e.g., disparity maps and optical flow images). Classifier  235  can then provide object classifications  213 —identifying obstacles and road hazards—to the AV control system  220 , which can react accordingly by operating the steering, braking, and acceleration systems  225  of the AV  200  to perform low level maneuvering. 
     Applying knowledge of the location and orientation of stereo camera  206  determined from sensor data  207 , the disparity mapper  211  can use the 3D environment data from the current sub-map  238  to generate a baseline disparity image that represents distances from the stereo camera  206  to known features of the environment. These features generally include terrain features, buildings, and other static, non-moving objects such as signs and trees. In some implementations, the disparity mapper  211  can generate the baseline disparity image through a ray casting algorithm that renders the three-dimensional environment into a two-dimensional image. Ray casting traces rays from a point corresponding to one of the stereo camera lenses, one ray for each pixel of the image sensor resolution, and finds the distance to the closest object blocking the path of that ray. The distances for all the rays cast make up a set of data that represents the baseline disparity image. In an environment with no new features or objects that are not included in the 3D environment data (e.g., pedestrians or other vehicles), the baseline disparity image should represent an accurate map of the distances from the stereo camera  206  to features and objects in the scene. 
     Using a pair of images  208  taken simultaneously of the scene, the disparity mapper  211  finds a set of points in one image which can be identified as the same points in the other image in order to create a disparity map. To do this, points (i.e., pixels) or features in one image are matched with the corresponding points or features in the other image, which can be done using standard algorithms comparing colors, lighting, etc. However, the construction of these disparity maps is computationally expensive because the search space for matching pixels between the two images is large, especially for high resolution images needed to accurately identify and classify objects in a scene. 
     Therefore, the disparity mapper  211  can use a combination of the baseline disparity image generated from the current sub-map  238  and the pair of images  208  acquired from the stereo camera  206  to narrow the search spaces. The disparity mapper  211  can efficiently output a disparity map of the scene that represents the distances from the stereo camera  206  to features and objects in the scene, including both existing features from the current sub-map  238  and new features and objects present in the scene that are not part of the current sub-map  238  data. In some implementations, the disparity mapper  211  matches pixels from the baseline disparity image to pixels in one of the stereo camera images. For example, disparity data corresponding to a pixel in the upper left corner of the left stereo camera image is taken from the baseline disparity image. Assuming that no new feature or object is present in the scene that is not included in the current sub-map  238 , the disparity data should be roughly equal (within a reasonable margin of error to account for map inaccuracies) to the disparity between pixels in the left and right stereo images taken of the scene. The disparity mapper  211  can then apply the baseline disparity image data to each pixel in the left image to determine a likely location of the corresponding pixel in the right image and reduce the search space of the correspondence algorithm. 
     Once corresponding pixels are found for each of the pixels in the left image, the disparity mapper  211  can output the generated disparity map (as image maps  218 ) for classifier  235  to use in classifying objects in the scene. In some aspects, an optical flow unit  212  can use the apparent motion of features in the field of view of the moving stereo camera  206  to supplement or replace the baseline disparity image generated from the 3D environment data. From either of the lenses of the stereo camera  206 , a map of optical flow vectors can be calculated between a previous frame and a current frame. The optical flow unit  212  can use these vectors to improve the correspondence search algorithm. For example, given the motion vector of a pixel in the left image from the stereo camera  206 , the motion vector of a corresponding pixel in the right image should be similar after accounting for the different perspective of the right lens of the stereo camera  206 . Furthermore, image maps  218  can include images of optical flow vectors that classifier  235  can use to improve object classifications  213 . 
     In accordance with aspects disclosed, the classifier  235  can also monitor situational data  217  from the data processing system  210  to identify potential areas of conflict. For example, the classifier  235  can monitor forward directional stereoscopic camera data or LIDAR data to identify areas of concern. In one example, the classifier  235  can utilize the current sub-map  238  to identify features along the current route traveled (e.g., as indicated by the route data  232 ), such as traffic signals, intersections, road signs, crosswalks, bicycle lanes, parking areas, and the like. As the AV  200  approaches such features or areas, the classifier  235  can monitor the forward situational data  217  to identify any external entities that may conflict with the operational flow of the AV  200 , such as pedestrians near a crosswalk or another vehicle approaching an intersection. 
     In many examples, while the AV control system  220  operates the steering, braking, and acceleration systems  225  along the current route on a high level, object classifications  213  provided to the AV control system  220  can indicate low level occurrences, such as obstacles and potential hazards, to which the AV control system  220  can make decisions and react. For example, object classifications  213  can indicate a pedestrian crossing the road, traffic signals, stop signs, other vehicles, road conditions, traffic conditions, bicycle lanes, crosswalks, pedestrian activity (e.g., a crowded adjacent sidewalk), and the like. The AV control system  220  can respond to different types of objects by generating control commands  221  to reactively operate the steering, braking, and acceleration systems  225  accordingly. 
     In many implementations, the AV control system  220  can receive a destination  219  from, for example, an interface system  215  of the AV  200 . The interface system  215  can include any number of touch-screens, voice sensors, mapping resources, etc., that enable a passenger  239  to provide a passenger input  241  indicating the destination  219 . For example, the passenger  239  can type the destination  219  into a mapping engine  275  of the AV  200 , or can speak the destination  219  into the interface system  215 . Additionally or alternatively, the interface system  215  can include a wireless communication module that can connect the AV  200  to a network  280  to communicate with a backend transport arrangement system  290  to receive invitations  282  to service a pick-up or drop-off request. Such invitations  282  can include the destination  219  (e.g., a pick-up location), and can be received by the AV  200  as a communication over the network  280  from the backend transport arrangement system  290 . In many aspects, the backend transport arrangement system  290  can manage routes and/or facilitate transportation for users using a fleet of autonomous vehicles throughout a given region. The backend transport arrangement system  290  can be operative to facilitate passenger pick-ups and drop-offs to generally service pick-up requests, facilitate delivery such as packages or food, and the like. 
     Based on the destination  219  (e.g., a pick-up location), the AV control system  220  can utilize the mapping engine  275  to receive route data  232  indicating a route to the destination  219 . In variations, the mapping engine  275  can also generate map content  226  dynamically indicating the route traveled to the destination  219 . The route data  232  and/or map content  226  can be utilized by the AV control system  220  to maneuver the AV  200  to the destination  219  along the selected route. For example, the AV control system  220  can dynamically generate control commands  221  for the autonomous vehicle&#39;s steering, braking, and acceleration system  225  to actively drive the AV  200  to the destination  219  along the selected route. Optionally, the map content  226  showing the current route traveled can be streamed to the interior interface system  215  so that the passenger(s)  239  can view the route and route progress in real time. 
     Methodology 
       FIG. 3  illustrates an example method of object classification in accordance with one or more embodiments.  FIG. 4  illustrates an example method of disparity mapping in accordance with one or more embodiments. While operations of these example implementations are described below as being performed by specific components, modules or systems of the AV  200 , it will be appreciated that these operations need not necessarily be performed by the specific components identified, and could be performed by a variety of components and modules, potentially distributed over a number of machines. Accordingly, references may be made to elements of AV  200  for the purpose of illustrating suitable components or elements for performing a step or sub step being described. Alternatively, at least certain ones of the variety of components and modules described in AV  200  can be arranged within a single hardware, software, or firmware component. It will also be appreciated that some of the steps of this method may be performed in parallel or in a different order than illustrated. 
     Referring to  FIG. 3 , a vehicle can obtain sensor data for the environment through, for example, proximity or touch sensors, remote detection sensors such as provided by radar or LIDAR, a stereo camera, and/or sonar sensors as described with respect to  FIGS. 1 and 2  ( 310 ). The vehicle can additionally obtain known data for the environment from previously recorded mapping resource data (i.e., sub-maps) that contain surface data for a given region. The vehicle can compare this sub-map data with the sensor data for the environment ( 320 ). The vehicle can then use the comparisons, including disparity maps and optical flow images, to create object classifications to assist the vehicle in maneuvering through road traffic to a particular destination ( 330 ). For example, a disparity mapper can utilize a current sub-map that includes recorded 3D LIDAR data and 3D stereo data of the current route traveled by the vehicle. The disparity mapper can continuously compare real-time sensor data to the data in the current sub-map to help a classifier identify potential hazards, such as pedestrians, other vehicles, bicyclists, etc. 
     Referring to  FIG. 4 , as the vehicle travels along a route, vehicle sensors can determine the location and orientation of the vehicle and its stereo camera. The sensors can determine latitude and longitude coordinates of the vehicle and a direction of travel, which can be further refined to identify the stereo camera&#39;s location in the world. For example, the vehicle&#39;s data processing system can retrieve sub-maps stored in a database of the vehicle or accessed remotely from the backend system via a network ( 410 ). The data processing system can use the 3D environment data stored in these sub-maps to perform localization and pose operations to determine a current location and orientation of the vehicle in relation to a given region (e.g., a city). Given the location of the stereo camera as it is disposed on the vehicle, the data processing system can further determine the precise location and orientation of the stereo camera in the world ( 412 ). 
     Applying knowledge of the location and orientation of the vehicle&#39;s stereo camera, a disparity mapper can use the 3D environment data to generate a baseline disparity image that represents distances from the stereo camera to known features of the environment ( 414 ). These features generally include terrain features, buildings, and other static, non-moving objects such as signs and trees. 
     In some implementations, the disparity mapper can generate the baseline disparity image through a ray casting algorithm that renders the three-dimensional environment into a two-dimensional image. Ray casting traces rays from a point corresponding to one of the stereo camera lenses, one ray for each pixel of the image sensor resolution, and finds the distance to the closest object blocking the path of that ray. The distances for all the rays cast make up a set of data that represents the baseline disparity image. In an environment with no new features or objects, such as pedestrians or other vehicles, that are not included in the 3D environment data, the baseline disparity image should represent an accurate map of the distances from the stereo camera to features and objects in the scene. 
     In another implementation, the disparity mapper can generate a baseline disparity image for each camera lens in the disparity mapping system using an alternate ray casting algorithm. The disparity mapper identifies 3D points in the 3D environment data that are visible to one or more camera lenses, and for each 3D point identified, the disparity mapper projects a ray from the 3D point to points representing each camera sensor in the disparity mapping system. The disparity mapper can then match each ray to 2D image coordinates for a baseline disparity image for that camera lens. 
     In order for the disparity mapper to create a disparity map for a scene that accounts for new and unexpected features and objects, the stereo camera takes a simultaneous pair of images of the scene, one for each lens of the stereo camera ( 420 ). In some implementations, the data processing system can remove distortions from the images and perform image rectification to reduce the pixel correspondence search space ( 422 ). In most camera configurations, finding correspondences between pixels in the two images requires a search in two dimensions. However, if the two lenses of a stereo camera are aligned correctly to be coplanar, the search is simplified to one dimension—a horizontal line parallel to the line between the lenses. Since it may be impractical to maintain perfect alignment between the lenses, the disparity mapper can perform image rectification to achieve similar results. Rectification can be performed using a variety of algorithms, such as planar rectification, cylindrical rectification, and polar rectification so that the rectified images have epipolar lines parallel to the horizontal axis and corresponding points with identical vertical coordinates. In implementations where rectification is performed, the disparity mapper can create the baseline disparity image in order to take the rectification into account for performance gains. 
     With a left and right image, horizontally displaced, taken simultaneously of the scene, the disparity mapper finds a set of points in one image which can be identified as the same points in the other image in order to create a disparity map. To do this, points (i.e., pixels) or features in one image are matched with the corresponding points or features in the other image, which can be done using standard algorithms comparing colors, lighting, etc. However, the construction of these disparity maps is computationally expensive because the search space for matching pixels between the two images is large, even when the images are rectified. 
     The disparity mapper can use a combination of the baseline disparity image from the 3D environment data and the pair of images acquired from the stereo camera to narrow the search spaces. In doing so, the disparity mapper can efficiently output a disparity map of the scene that represents the distances from the stereo camera to features and objects in the scene, including both existing features from the 3D environment data and new features and objects present in the scene that are not part of the 3D environment data. In some implementations, the disparity mapper iterates through the pixels in the baseline disparity image and compares the pixel data in the baseline disparity image to its corresponding pixel in the same 2D location in one of the stereo camera images (typically the left image, but the right image can be used instead) ( 430 ). For example, disparity data corresponding to a pixel in the upper left corner of the left stereo camera image is taken from the baseline disparity image. Assuming that no new feature or object is present in the scene that is not included in the 3D environment data, the disparity data should be roughly equal (within a reasonable margin of error to account for map inaccuracies) to the disparity between pixels in the left and right stereo images taken of the scene. 
     Therefore, the disparity mapper can apply the baseline disparity image data to each pixel in the left image to determine a likely location of the corresponding pixel in the right image ( 432 ). This baseline disparity image data contains a disparity value for each pixel that can be added to or subtracted from the 2D coordinates of the pixel to find the likely 2D coordinates for the corresponding pixel in the right image ( 434 ). Depending on rectification steps taken for the stereo camera images, the coordinate search may be performed on a single dimension. 
     For a given pixel in the left image, if the baseline disparity image data indicates that the pixel should have a large disparity with its corresponding pixel in the right image (which would mean there is a nearby feature or object in the 3D environment data in the space that pixel represents), the disparity mapper starts the search for the corresponding pixel in the right image at a large coordinate distance from where the left pixel is located in the left image. If the baseline disparity image data indicates that the pixel should have a small disparity with its corresponding pixel in the right image (which would mean there are no nearby features or objects in the 3D environment data in the space that pixel represents), the disparity mapper starts the search for the corresponding pixel in the right image at a small coordinate distance from where the left pixel is located in the left image. This likely location used for the start of the correspondence search should be the correct corresponding pixel in conditions where the map is perfect and there are no changes from the environment recorded in the 3D environment data to the actual scene in the present. Therefore, given images of a scene that mostly matches the 3D environment data, the disparity mapper can accurately locate corresponding pixels in the right image without further searching. Where portions of the images of the scene do not match the 3D environment data (e.g., there is a moving object such as a pedestrian or vehicle in the scene), the disparity mapper may still reduce the search space using the determined likely location of the corresponding pixel. For example, the disparity mapper can search the pixel at the likely location and any pixels at a programmed distance around the likely location to find the most probable corresponding pixel. 
     In some aspects, disparity maps calculated for previous images from the stereo camera can be used to supplement or replace the baseline disparity image generated from the 3D environment data. A vehicle equipped with a stereo camera may collect images fast enough such that the camera location does not change significantly from frame to frame and the background remains mostly static. Therefore, the prior disparity map generated for a previous frame can be useful to initialize the subsequent map. Only near range discontinuities and moving objects should result in large disparity differences. Everywhere else, the disparity values should be similar. In addition, the disparity mapper can incorporate information about the stereo camera&#39;s movement, which can be taken from other sensors on a vehicle, to modify the prior disparity map such that it represents a more accurate baseline disparity image for the stereo camera&#39;s current position. For example, if sensors on the vehicle indicate that the vehicle, and therefore the stereo camera, has travelled one meter forward since the last frame was taken and previous disparity map calculated, the disparity mapper can adjust the previous disparity map to take that one meter movement into account in generating a baseline disparity map. The disparity mapper can then use the generated baseline disparity map to reduce the search space for corresponding pixels between the current pair of stereo camera images. 
     In another aspect, an optical flow unit can use the apparent motion of features in the field of view of a moving stereo camera to supplement or replace the baseline disparity image generated from the 3D environment data. From either of the lenses of the stereo camera, a map of optical flow vectors can be calculated between a previous frame and a current frame. The optical flow unit can use these vectors to improve the correspondence search algorithm in similar ways to using previously calculated disparity maps. For example, given the motion vector of a pixel in the left image from the stereo camera, the motion vector of a corresponding pixel in the right image should be similar after accounting for the different perspective of the right lens of the stereo camera. 
     After a corresponding pixel is found in the right image, the disparity mapper can proceed to match remaining pixels ( 436 ). In some implementations, the disparity mapper can perform matching on multiple pixels simultaneously, either through separate processes or by grouping pixels together. Once corresponding pixels are found for each of the pixels in the left image, the disparity mapper can output the generated disparity map for other systems to use ( 438 ). For example, an object classifier can use the generated disparity map to classify objects in the scene based on the disparity map and other data. 
     Hardware Diagram 
       FIG. 5  is a block diagram illustrating a computer system upon which examples described herein may be implemented. For example, the data processing system  210  and classifier  235  shown and described in  FIG. 2  may be implemented on the computer system  500  of  FIG. 5 . The computer system  500  can be implemented using one or more processors  504 , and one or more memory resources  506 . 
     According to some examples, the computer system  500  may be implemented within an autonomous vehicle with software and hardware resources such as described with examples of  FIGS. 1 and 2 . In an example shown, the computer system  500  can be distributed spatially into various regions of the autonomous vehicle, with various aspects integrated with other components of the autonomous vehicle itself. For example, the processors  504  and/or memory resources  506  can be provided in the trunk of the autonomous vehicle. The various processing resources  504  of the computer system  500  can also execute disparity mapping instructions  512  using microprocessors or integrated circuits. In some examples, the disparity mapping instructions  512  can be executed by the processing resources  504  or using field-programmable gate arrays (FPGAs). 
     In an example of  FIG. 5 , the computer system  500  can include a local communication interface  550  (or series of local links) to vehicle interfaces and other resources of the autonomous vehicle (e.g., the computer stack drives). In one implementation, the communication interface  550  provides a data bus or other local links to electro-mechanical interfaces of the vehicle, such as wireless or wired links to the AV control system  220 . 
     The memory resources  506  can include, for example, main memory, a read-only memory (ROM), storage device, and cache resources. The main memory of memory resources  506  can include random access memory (RAM) or other dynamic storage device, for storing information and instructions which are executable by the processors  504 . The processors  504  can execute instructions for processing information stored with the main memory of the memory resources  506 . The main memory  506  can also store temporary variables or other intermediate information which can be used during execution of instructions by one or more of the processors  504 . The memory resources  506  can also include ROM or other static storage device for storing static information and instructions for one or more of the processors  504 . The memory resources  506  can also include other forms of memory devices and components, such as a magnetic disk or optical disk, for purpose of storing information and instructions for use by one or more of the processors  504 . 
     According to some examples, the memory  506  may store a plurality of software instructions including, for example, disparity mapping instructions  512 . The disparity mapping instructions  512  may be executed by one or more of the processors  504  in order to implement functionality such as described with respect to the disparity mapper  211 , optical flow unit  212 , and classifier  235  of  FIG. 2 . 
     In certain examples, the computer system  500  can receive sensor data  562  over the communication interface  550  from various AV subsystems  560  (e.g., the AV control system  220  or data processing system  210 ). In executing the disparity mapping instructions  512 , the processing resources  504  can monitor the sensor data  562  and generate object classifications that the AV control system  220  can use to send commands to the output systems  520  of the AV  200  in accordance with examples described herein. 
     It is contemplated for examples described herein to extend to individual elements and concepts described herein, independently of other concepts, ideas or systems, as well as for examples to include combinations of elements recited anywhere in this application. Although examples are described in detail herein with reference to the accompanying drawings, it is to be understood that the concepts are not limited to those precise examples. As such, many modifications and variations will be apparent to practitioners skilled in this art. Accordingly, it is intended that the scope of the concepts be defined by the following claims and their equivalents. Furthermore, it is contemplated that a particular feature described either individually or as part of an example can be combined with other individually described features, or parts of other examples, even if the other features and examples make no mentioned of the particular feature. Thus, the absence of describing combinations should not preclude claiming rights to such combinations.