Patent Publication Number: US-11022974-B2

Title: Sensor-based object-detection optimization for autonomous vehicles

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 14/756,991, filed Nov. 4, 2015, entitled “SENSOR-BASED OBJECT-DETECTION OPTIMIZATION FOR AUTONOMOUS VEHICLES,” the entirety of which is incorporated herein by reference. 
    
    
     FIELD 
     Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. 
     BACKGROUND 
     A variety of approaches to developing driverless vehicles focus predominately on automating conventional vehicles (e.g., manually-driven automotive vehicles) with an aim toward producing driverless vehicles for consumer purchase. For example, a number of automotive companies and affiliates are modifying conventional automobiles and control mechanisms, such as steering, to provide consumers with an ability to own a vehicle that may operate without a driver. In some approaches, a conventional driverless vehicle performs safety-critical driving functions in some conditions, but requires a driver to assume control (e.g., steering, etc.) should the vehicle controller fail to resolve certain issues that might jeopardize the safety of the occupants. 
     Although functional, conventional driverless vehicles typically have a number of drawbacks. For example, a large number of driverless cars under development have evolved from vehicles requiring manual (i.e., human-controlled) steering and other like automotive functions. Therefore, a majority of driverless cars are based on a paradigm that a vehicle is to be designed to accommodate a licensed driver, for which a specific seat or location is reserved within the vehicle. As such, driverless vehicles are designed sub-optimally and generally forego opportunities to simplify vehicle design and conserve resources (e.g., reducing costs of producing a driverless vehicle). Other drawbacks are also present in conventional driverless vehicles. 
     Other drawbacks are also present in conventional transportation services, which are not well-suited for managing, for example, inventory of vehicles effectively due to the common approaches of providing conventional transportation and ride-sharing services. In one conventional approach, passengers are required to access a mobile application to request transportation services via a centralized service that assigns a human driver and vehicle (e.g., under private ownership) to a passenger. With the use of differently-owned vehicles, maintenance of private vehicles and safety systems generally go unchecked. In another conventional approach, some entities enable ride-sharing for a group of vehicles by allowing drivers, who enroll as members, access to vehicles that are shared among the members. This approach is not well-suited to provide for convenient transportation services as drivers need to pick up and drop off shared vehicles at specific locations, which typically are rare and sparse in city environments, and require access to relatively expensive real estate (i.e., parking lots) at which to park ride-shared vehicles. In the above-described conventional approaches, the traditional vehicles used to provide transportation services are generally under-utilized, from an inventory perspective, as the vehicles are rendered immobile once a driver departs. Further, ride-sharing approaches (as well as individually-owned vehicle transportation services) generally are not well-suited to rebalance inventory to match demand of transportation services to accommodate usage and typical travel patterns. Note, too, that some conventionally-described vehicles having limited self-driving automation capabilities also are not well-suited to rebalance inventories as a human driver generally may be required. Examples of vehicles having limited self-driving automation capabilities are vehicles designated as Level 3 (“L3”) vehicles, according to the U.S. Department of Transportation&#39;s National Highway Traffic Safety Administration (“NHTSA”). 
     As another drawback, typical approaches to driverless vehicles are generally not well-suited to detect and navigate vehicles relative to interactions (e.g., social interactions) between a vehicle-in-travel and other drivers of vehicles or individuals. For example, some conventional approaches are not sufficiently able to identify pedestrians, cyclists, etc., and associated interactions, such as eye contact, gesturing, and the like, for purposes of addressing safety risks to occupants of a driverless vehicles, as well as drivers of other vehicles, pedestrians, etc. 
     Thus, what is needed is a solution for facilitating an implementation of autonomous vehicles, without the limitations of conventional techniques. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings: 
         FIG. 1  is a diagram depicting implementation of a fleet of autonomous vehicles that are communicatively networked to an autonomous vehicle service platform, according to some embodiments; 
         FIG. 2  is an example of a flow diagram to monitor a fleet of autonomous vehicles, according to some embodiments; 
         FIG. 3A  is a diagram depicting examples of sensors and other autonomous vehicle components, according to some examples; 
         FIGS. 3B to 3E  are diagrams depicting examples of sensor field redundancy and autonomous vehicle adaption to a loss of a sensor field, according to some examples; 
         FIG. 4  is a functional block diagram depicting a system including an autonomous vehicle service platform that is communicatively coupled via a communication layer to an autonomous vehicle controller, according to some examples; 
         FIG. 5  is an example of a flow diagram to control an autonomous vehicle, according to some embodiments; 
         FIG. 6  is a diagram depicting an example of an architecture for an autonomous vehicle controller, according to some embodiments; 
         FIG. 7  is a diagram depicting an example of an autonomous vehicle service platform implementing redundant communication channels to maintain reliable communications with a fleet of autonomous vehicles, according to some embodiments; 
         FIG. 8  is a diagram depicting an example of a messaging application configured to exchange data among various applications, according to some embodiment; 
         FIG. 9  is a diagram depicting types of data for facilitating teleoperations using a communications protocol described in  FIG. 8 , according to some examples; 
         FIG. 10  is a diagram illustrating an example of a teleoperator interface with which a teleoperator may influence path planning, according to some embodiments; 
         FIG. 11  is a diagram depicting an example of a planner configured to invoke teleoperations, according to some examples; 
         FIG. 12  is an example of a flow diagram configured to control an autonomous vehicle, according to some embodiments; 
         FIG. 13  depicts an example in which a planner may generate a trajectory, according to some examples; 
         FIG. 14  is a diagram depicting another example of an autonomous vehicle service platform, according to some embodiments; 
         FIG. 15  is an example of a flow diagram to control an autonomous vehicle, according to some embodiments; 
         FIG. 16  is a diagram of an example of an autonomous vehicle fleet manager implementing a fleet optimization manager, according to some examples; 
         FIG. 17  is an example of a flow diagram for managing a fleet of autonomous vehicles, according to some embodiments; 
         FIG. 18  is a diagram illustrating an autonomous vehicle fleet manager implementing an autonomous vehicle communications link manager, according to some embodiments; 
         FIG. 19  is an example of a flow diagram to determine actions for autonomous vehicles during an event, according to some embodiments; 
         FIG. 20  is a diagram depicting an example of a localizer, according to some embodiments; 
         FIG. 21  is an example of a flow diagram to generate local pose data based on integrated sensor data, according to some embodiments; 
         FIG. 22  is a diagram depicting another example of a localizer, according to some embodiments; 
         FIG. 23  is a diagram depicting an example of a perception engine, according to some embodiments; 
         FIG. 24  is an example of a flow chart to generate perception engine data, according to some embodiments; 
         FIG. 25  is an example of a segmentation processor, according to some embodiments; 
         FIG. 26A  is a diagram depicting examples of an object tracker and a classifier, according to various embodiments; 
         FIG. 26B  is a diagram depicting another example of an object tracker according to at least some examples; 
         FIG. 27  is an example of front-end processor for a perception engine, according to some examples; 
         FIG. 28  is a diagram depicting a simulator configured to simulate an autonomous vehicle in a synthetic environment, according to various embodiments; 
         FIG. 29  is an example of a flow chart to simulate various aspects of an autonomous vehicle, according to some embodiments; 
         FIG. 30  is an example of a flow chart to generate map data, according to some embodiments; 
         FIG. 31  is a diagram depicting an architecture of a mapping engine, according to some embodiments 
         FIG. 32  is a diagram depicting an autonomous vehicle application, according to some examples; 
         FIGS. 33 to 35  illustrate examples of various computing platforms configured to provide various functionalities to components of an autonomous vehicle service, according to various embodiments; 
         FIGS. 36A to 36B  illustrate a high-level block diagram depicting an autonomous vehicle system having a sensor-based anomaly while in operation, according to various embodiments; 
         FIG. 37  illustrates a high-level block diagram of a sensor-based object detection optimization for autonomous vehicles, according to various embodiments; 
         FIG. 38  is a network diagram of a system for sensor-based object detection optimization for autonomous vehicles, showing a block diagram of an autonomous vehicle management system, according to an embodiment; 
         FIG. 39  is a high-level flow diagram illustrating a process for sensor-based object detection optimization for autonomous vehicles, according to some examples; and 
         FIGS. 40 and 41  illustrate exemplary computing platforms disposed in devices configured to optimize sensor-based object detection in accordance with various embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments or examples may be implemented in numerous ways, including as a system, a process, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims. 
     A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims, and numerous alternatives, modifications, and equivalents thereof. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description. 
       FIG. 1  is a diagram depicting an implementation of a fleet of autonomous vehicles that are communicatively networked to an autonomous vehicle service platform, according to some embodiments. Diagram  100  depicts a fleet of autonomous vehicles  109  (e.g., one or more of autonomous vehicles  109   a  to  109   e ) operating as a service, each autonomous vehicle  109  being configured to self-drive a road network  110  and establish a communication link  192  with an autonomous vehicle service platform  101 . In examples in which a fleet of autonomous vehicles  109  constitutes a service, a user  102  may transmit a request  103  for autonomous transportation via one or more networks  106  to autonomous vehicle service platform  101 . In response, autonomous vehicle service platform  101  may dispatch one of autonomous vehicles  109  to transport user  102  autonomously from geographic location  119  to geographic location  111 . Autonomous vehicle service platform  101  may dispatch an autonomous vehicle from a station  190  to geographic location  119 , or may divert an autonomous vehicle  109   c , already in transit (e.g., without occupants), to service the transportation request for user  102 . Autonomous vehicle service platform  101  may be further configured to divert an autonomous vehicle  109   c  in transit, with passengers, responsive to a request from user  102  (e.g., as a passenger). In addition, autonomous vehicle service platform  101  may be configured to reserve an autonomous vehicle  109   c  in transit, with passengers, for diverting to service a request of user  102  subsequent to dropping off existing passengers. Note that multiple autonomous vehicle service platforms  101  (not shown) and one or more stations  190  may be implemented to service one or more autonomous vehicles  109  in connection with road network  110 . One or more stations  190  may be configured to store, service, manage, and/or maintain an inventory of autonomous vehicles  109  (e.g., station  190  may include one or more computing devices implementing autonomous vehicle service platform  101 ). 
     According to some examples, at least some of autonomous vehicles  109   a  to  109   e  are configured as bidirectional autonomous vehicles, such as bidirectional autonomous vehicle (“AV”)  130 . Bidirectional autonomous vehicle  130  may be configured to travel in either direction principally along, but not limited to, a longitudinal axis  131 . Accordingly, bidirectional autonomous vehicle  130  may be configured to implement active lighting external to the vehicle to alert others (e.g., other drivers, pedestrians, cyclists, etc.) in the adjacent vicinity, and a direction in which bidirectional autonomous vehicle  130  is traveling. For example, active sources of light  136  may be implemented as active lights  138   a  when traveling in a first direction, or may be implemented as active lights  138   b  when traveling in a second direction. Active lights  138   a  may be implemented using a first subset of one or more colors, with optional animation (e.g., light patterns of variable intensities of light or color that may change over time). Similarly, active lights  138   b  may be implemented using a second subset of one or more colors and light patterns that may be different than those of active lights  138   a . For example, active lights  138   a  may be implemented using white-colored lights as “headlights,” whereas active lights  138   b  may be implemented using red-colored lights as “taillights.” Active lights  138   a  and  138   b , or portions thereof, may be configured to provide other light-related functionalities, such as provide “turn signal indication” functions (e.g., using yellow light). According to various examples, logic in autonomous vehicle  130  may be configured to adapt active lights  138   a  and  138   b  to comply with various safety requirements and traffic regulations or laws for any number of jurisdictions. 
     In some embodiments, bidirectional autonomous vehicle  130  may be configured to have similar structural elements and components in each quad portion, such as quad portion  194 . The quad portions are depicted, at least in this example, as portions of bidirectional autonomous vehicle  130  defined by the intersection of a plane  132  and a plane  134 , both of which pass through the vehicle to form two similar halves on each side of planes  132  and  134 . Further, bidirectional autonomous vehicle  130  may include an autonomous vehicle controller  147  that includes logic (e.g., hardware or software, or as combination thereof) that is configured to control a predominate number of vehicle functions, including driving control (e.g., propulsion, steering, etc.) and active sources  136  of light, among other functions. Bidirectional autonomous vehicle  130  also includes a number of sensors  139  disposed at various locations on the vehicle (other sensors are not shown). 
     Autonomous vehicle controller  147  may be further configured to determine a local pose (e.g., local position) of an autonomous vehicle  109  and to detect external objects relative to the vehicle. For example, consider that bidirectional autonomous vehicle  130  is traveling in the direction  119  in road network  110 . A localizer (not shown) of autonomous vehicle controller  147  can determine a local pose at the geographic location  111 . As such, the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings  115  and  117 , which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose. Further, a perception engine (not shown) of autonomous vehicle controller  147  may be configured to detect, classify, and predict the behavior of external objects, such as external object  112  (a “tree”) and external object  114  (a “pedestrian”). Classification of such external objects may broadly classify objects as static objects, such as external object  112 , and dynamic objects, such as external object  114 . The localizer and the perception engine, as well as other components of the AV controller  147 , collaborate to cause autonomous vehicles  109  to drive autonomously. 
     According to some examples, autonomous vehicle service platform  101  is configured to provide teleoperator services should an autonomous vehicle  109  request teleoperation. For example, consider that an autonomous vehicle controller  147  in autonomous vehicle  109   d  detects an object  126  obscuring a path  124  on roadway  122  at point  191 , as depicted in inset  120 . If autonomous vehicle controller  147  cannot ascertain a path or trajectory over which vehicle  109   d  may safely transit with a relatively high degree of certainty, then autonomous vehicle controller  147  may transmit request message  105  for teleoperation services. In response, a teleoperator computing device  104  may receive instructions from a teleoperator  108  to perform a course of action to successfully (and safely) negotiate obstacles  126 . Response data  107  then can be transmitted back to autonomous vehicle  109   d  to cause the vehicle to, for example, safely cross a set of double lines as it transits along the alternate path  121 . In some examples, teleoperator computing device  104  may generate a response identifying geographic areas to exclude from planning a path. In particular, rather than provide a path to follow, a teleoperator  108  may define areas or locations that the autonomous vehicle must avoid. 
     In view of the foregoing, the structures and/or functionalities of autonomous vehicle  130  and/or autonomous vehicle controller  147 , as well as their components, can perform real-time (or near real-time) trajectory calculations through autonomous-related operations, such as localization and perception, to enable autonomous vehicles  109  to self-drive. 
     In some cases, the bidirectional nature of bidirectional autonomous vehicle  130  provides for a vehicle that has quad portions  194  (or any other number of symmetric portions) that are similar or are substantially similar to each other. Such symmetry reduces complexity of design and decreases relatively the number of unique components or structures, thereby reducing inventory and manufacturing complexities. For example, a drivetrain and wheel system may be disposed in any of the quad portions  194 . Further, autonomous vehicle controller  147  is configured to invoke teleoperation services to reduce the likelihood that an autonomous vehicle  109  is delayed in transit while resolving an event or issue that may otherwise affect the safety of the occupants. In some cases, the visible portion of road network  110  depicts a geo-fenced region that may limit or otherwise control the movement of autonomous vehicles  109  to the road network shown in  FIG. 1 . According to various examples, autonomous vehicle  109 , and a fleet thereof, may be configurable to operate as a level 4 (“full self-driving automation,” or L4) vehicle that can provide transportation on demand with the convenience and privacy of point-to-point personal mobility while providing the efficiency of shared vehicles. In some examples, autonomous vehicle  109 , or any autonomous vehicle described herein, may be configured to omit a steering wheel or any other mechanical means of providing manual (i.e., human-controlled) steering for autonomous vehicle  109 . Further, autonomous vehicle  109 , or any autonomous vehicle described herein, may be configured to omit a seat or location reserved within the vehicle for an occupant to engage a steering wheel. 
       FIG. 2  is an example of a flow diagram to monitor a fleet of autonomous vehicles, according to some embodiments. At  202 , flow  200  begins when a fleet of autonomous vehicles are monitored. At least one autonomous vehicle includes an autonomous vehicle controller configured to cause the vehicle to autonomously transit from a first geographic region to a second geographic region. At  204 , data representing an event associated with a calculated confidence level for a vehicle is detected. An event may be a condition or situation affecting operation, or potentially affecting operation, of an autonomous vehicle. The events may be internal to an autonomous vehicle, or external. For example, an obstacle obscuring a roadway may be viewed as an event, as well as a reduction or loss of communication. An event may include traffic conditions or congestion, as well as unexpected or unusual numbers or types of external objects (or tracks) that are perceived by a perception engine. An event may include weather-related conditions (e.g., loss of friction due to ice or rain) or the angle at which the sun is shining (e.g., at sunset), such as low angle to the horizon that cause sun to shine brightly in the eyes of human drivers of other vehicles. These and other conditions may be viewed as events that cause invocation of the teleoperator service or for the vehicle to execute a safe-stop trajectory. 
     At  206 , data representing a subset of candidate trajectories may be received from an autonomous vehicle responsive to the detection of the event. For example, a planner of an autonomous vehicle controller may calculate and evaluate large numbers of trajectories (e.g., thousands or greater) per unit time, such as a second. In some embodiments, candidate trajectories are a subset of the trajectories that provide for relatively higher confidence levels that an autonomous vehicle may move forward safely in view of the event (e.g., using an alternate path provided by a teleoperator). Note that some candidate trajectories may be ranked or associated with higher degrees of confidence than other candidate trajectories. According to some examples, subsets of candidate trajectories may originate from any number of sources, such as a planner, a teleoperator computing device (e.g., teleoperators can determine and provide approximate paths), etc., and may be combined as a superset of candidate trajectories. At  208 , path guidance data may be identified at one or more processors. The path guidance data may be configured to assist a teleoperator in selecting a guided trajectory from one or more of the candidate trajectories. In some instances, the path guidance data specifies a value indicative of a confidence level or probability that indicates the degree of certainty that a particular candidate trajectory may reduce or negate the probability that the event may impact operation of an autonomous vehicle. A guided trajectory, as a selected candidate trajectory, may be received at  210 , responsive to input from a teleoperator (e.g., a teleoperator may select at least one candidate trajectory as a guided trajectory from a group of differently-ranked candidate trajectories). The selection may be made via an operator interface that lists a number of candidate trajectories, for example, in order from highest confidence levels to lowest confidence levels. At  212 , the selection of a candidate trajectory as a guided trajectory may be transmitted to the vehicle, which, in turn, implements the guided trajectory for resolving the condition by causing the vehicle to perform a teleoperator-specified maneuver. As such, the autonomous vehicle may transition from a non-normative operational state. 
       FIG. 3A  is a diagram depicting examples of sensors and other autonomous vehicle components, according to some examples. Diagram  300  depicts an interior view of a bidirectional autonomous vehicle  330  that includes sensors, signal routers  345 , drive trains  349 , removable batteries  343 , audio generators  344  (e.g., speakers or transducers), and autonomous vehicle (“AV”) control logic  347 . Sensors shown in diagram  300  include image capture sensors  340  (e.g., light capture devices or cameras of any type), audio capture sensors  342  (e.g., microphones of any type), radar devices  348 , sonar devices  341  (or other like sensors, including ultrasonic sensors or acoustic-related sensors), and LIDAR devices  346 , among other sensor types and modalities (some of which are not shown, such inertial measurement units, or “IMUs,” global positioning system (“GPS”) sensors, sonar sensors, etc.). Note that quad portion  350  is representative of the symmetry of each of four “quad portions” of bidirectional autonomous vehicle  330  (e.g., each quad portion  350  may include a wheel, a drivetrain  349 , similar steering mechanisms, similar structural support and members, etc. beyond that which is depicted). As depicted in  FIG. 3A , similar sensors may be placed in similar locations in each quad portion  350 , however any other configuration may implemented. Each wheel may be steerable individually and independent of the others. Note, too, that removable batteries  343  may be configured to facilitate being swapped in and swapped out rather than charging in situ, thereby ensuring reduced or negligible downtimes due to the necessity of charging batteries  343 . While autonomous vehicle controller is depicted as being used in a bidirectional autonomous vehicle  330 , autonomous vehicle controller is not so limited and may be implemented in unidirectional autonomous vehicles or any other type of vehicle, whether on land, in air, or at sea. Note that the depicted and described positions, locations, orientations, quantities, and types of sensors shown in  FIG. 3A  are not intended to be limiting, and, as such, there may be any number and type of sensor, and any sensor may be located and oriented anywhere on autonomous vehicle  330 . 
     According to some embodiments, portions of the autonomous vehicle (“AV”) control logic  347  may be implemented using clusters of graphics processing units (“GPUs”) implementing a framework and programming model suitable for programming the clusters of GPUs. For example, a compute unified device architecture (“CUDA”) compatible programming language and application programming interface (“API”) model may be used to program the GPUs. CUDA™ is produced and maintained by NVIDIA of Santa Clara, Calif. Note that other programming languages may be implemented, such as OpenCL, or any other parallel programming language. 
     According to some embodiments, autonomous vehicle control logic  347  may be implemented in hardware and/or software as autonomous vehicle controller  347   a , which is shown to include a motion controller  362 , a planner  364 , a perception engine  366 , and a localizer  368 . As shown, autonomous vehicle controller  347   a  is configured to receive camera data  340   a . LIDAR data  346   a , and radar data  348   a , or any other range-sensing or localization data, including sonar data  341   a  or the like. Autonomous vehicle controller  347   a  is also configured to receive positioning data, such as GPS data  352 , IMU data  354 , and other position-sensing data (e.g., wheel-related data, such as steering angles, angular velocity, etc.). Further, autonomous vehicle controller  347   a  may receive any other sensor data  356 , as well as reference data  339 . In some cases, reference data  339  includes map data (e.g., 3D map data, 2D map data, 4D map data (e.g., including Epoch Determination)) and route data (e.g., road network data, including, but not limited to, RNDF data (or similar data), MDF data (or similar data), etc. 
     Localizer  368  is configured to receive sensor data from one or more sources, such as GPS data  352 , wheel data, IMU data  354 , LIDAR data  346   a , camera data  340   a , radar data  348   a , and the like, as well as reference data  339  (e.g., 3D map data and route data). Localizer  368  integrates (e.g., fuses the sensor data) and analyzes the data by comparing sensor data to map data to determine a local pose (or position) of bidirectional autonomous vehicle  330 . According to some examples, localizer  368  may generate or update the pose or position of any autonomous vehicle in real-time or near real-time. Note that localizer  368  and its functionality need not be limited to “bi-directional” vehicles and can be implemented in any vehicle of any type. Therefore, localizer  368  (as well as other components of AV controller  347   a ) may be implemented in a “unidirectional” vehicle or any non-autonomous vehicle. According to some embodiments, data describing a local pose may include one or more of an x-coordinate, a y-coordinate, a z-coordinate (or any coordinate of any coordinate system, including polar or cylindrical coordinate systems, or the like), a yaw value, a roll value, a pitch value (e.g., an angle value), a rate (e.g., velocity), altitude, and the like. 
     Perception engine  366  is configured to receive sensor data from one or more sources, such as LIDAR data  346   a , camera data  340   a , radar data  348   a , and the like, as well as local pose data. Perception engine  366  may be configured to determine locations of external objects based on sensor data and other data. External objects, for instance, may be objects that are not part of a drivable surface. For example, perception engine  366  may be able to detect and classify external objects as pedestrians, bicyclists, dogs, other vehicles, etc. (e.g., perception engine  366  is configured to classify the objects in accordance with a type of classification, which may be associated with semantic information, including a label). Based on the classification of these external objects, the external objects may be labeled as dynamic objects or static objects. For example, an external object classified as a tree may be labeled as a static object, while an external object classified as a pedestrian may be labeled as a static object. External objects labeled as static mayor may not be described in map data. Examples of external objects likely to be labeled as static include traffic cones, cement barriers arranged across a roadway, lane closure signs, newly-placed mailboxes or trash cans adjacent a roadway, etc. Examples of external objects likely to be labeled as dynamic include bicyclists, pedestrians, animals, other vehicles, etc. If the external object is labeled as dynamic, and further data about the external object may indicate a typical level of activity and velocity, as well as behavior patterns associated with the classification type. Further data about the external object may be generated by tracking the external object. As such, the classification type can be used to predict or otherwise determine the likelihood that an external object may, for example, interfere with an autonomous vehicle traveling along a planned path. For example, an external object that is classified as a pedestrian may be associated with some maximum speed, as well as an average speed (e.g., based on tracking data). The velocity of the pedestrian relative to the velocity of an autonomous vehicle can be used to determine if a collision is likely. Further, perception engine  364  may determine a level of uncertainty associated with a current and future state of objects. In some examples, the level of uncertainty may be expressed as an estimated value (or probability). 
     Planner  364  is configured to receive perception data from perception engine  366 , and may also include localizer data from localizer  368 . According to some examples, the perception data may include an obstacle map specifying static and dynamic objects located in the vicinity of an autonomous vehicle, whereas the localizer data may include a local pose or position. In operation, planner  364  generates numerous trajectories, and evaluates the trajectories, based on at least the location of the autonomous vehicle against relative locations of external dynamic and static objects. Planner  364  selects an optimal trajectory based on a variety of criteria over which to direct the autonomous vehicle in way that provides for collision-free travel. In some examples, planner  364  may be configured to calculate the trajectories as probabilistically-determined trajectories. Further, planner  364  may transmit steering and propulsion commands (as well as decelerating or braking commands) to motion controller  362 . Motion controller  362  subsequently may convert any of the commands, such as a steering command, a throttle or propulsion command, and a braking command, into control signals (e.g., for application to actuators or other mechanical interfaces) to implement changes in steering or wheel angles  351  and/or velocity  353 . 
       FIGS. 3B to 3E  are diagrams depicting examples of sensor field redundancy and autonomous vehicle adaption to a loss of a sensor field, according to some examples. Diagram  391  of  FIG. 3B  depicts a sensor field  301   a  in which sensor  310   a  detects objects (e.g., for determining range or distance, or other information). While sensor  310   a  may implement any type of sensor or sensor modality, sensor  310   a  and similarly-described sensors, such as sensors  310   b ,  310   c , and  310   d , may include LIDAR devices. Therefore, sensor fields  301   a ,  301   b ,  301   c , and  301   d  each includes a field into which lasers extend. Diagram  392  of  FIG. 3C  depicts four overlapping sensor fields each of which is generated by a corresponding LIDAR sensor  310  (not shown). As shown, portions  301  of the sensor fields include no overlapping sensor fields (e.g., a single LIDAR field), portions  302  of the sensor fields include two overlapping sensor fields, and portions  303  include three overlapping sensor fields, whereby such sensors provide for multiple levels of redundancy should a LIDAR sensor fail. 
       FIG. 3D  depicts a loss of a sensor field due to failed operation of LIDAR  309 , according to some examples. Sensor field  302  of  FIG. 3C  is transformed into a single sensor field  305 , one of sensor fields  301  of  FIG. 3C  is lost to a gap  304 , and three of sensor fields  303  of  FIG. 3C  are transformed into sensor fields  306  (i.e., limited to two overlapping fields). Should autonomous car  330   c  be traveling in the direction of travel  396 , the sensor field in front of the moving autonomous vehicle may be less robust than the one at the trailing end portion. According to some examples, an autonomous vehicle controller (not shown) is configured to leverage the bidirectional nature of autonomous vehicle  330   c  to address the loss of sensor field at the leading area in front of the vehicle.  FIG. 3E  depicts a bidirectional maneuver for restoring a certain robustness of the sensor field in front of autonomous vehicle  330   d . As shown, a more robust sensor field  302  is disposed at the rear of the vehicle  330   d  coextensive with taillights  348 . When convenient, autonomous vehicle  330   d  performs a bidirectional maneuver by pulling into a driveway  397  and switches its directionality such that taillights  348  actively switch to the other side (e.g., the trailing edge) of autonomous vehicle  330   d . As shown, autonomous vehicle  330   d  restores a robust sensor field  302  in front of the vehicle as it travels along direction of travel  398 . Further, the above-described bidirectional maneuver obviates a requirement for a more complicated maneuver that requires backing up into a busy roadway. 
       FIG. 4  is a functional block diagram depicting a system including an autonomous vehicle service platform that is communicatively coupled via a communication layer to an autonomous vehicle controller, according to some examples. Diagram  400  depicts an autonomous vehicle controller (“AV”)  447  disposed in an autonomous vehicle  430 , which, in turn, includes a number of sensors  470  coupled to autonomous vehicle controller  447 . Sensors  470  include one or more LIDAR devices  472 , one or more cameras  474 , one or more radars  476 , one or more global positioning system (“GPS”) data receiver-sensors, one or more inertial measurement units (“IMUs”)  475 , one or more odometry sensors  477  (e.g., wheel encoder sensors, wheel speed sensors, and the like), and any other suitable sensors  478 , such as infrared cameras or sensors, hyperspectral-capable sensors, ultrasonic sensors (or any other acoustic energy-based sensor), radio frequency-based sensors, etc. In some cases, wheel angle sensors configured to sense steering angles of wheels may be included as odometry sensors  477  or suitable sensors  478 . In a non-limiting example, autonomous vehicle controller  447  may include four or more LIDARs  472 , sixteen or more cameras  474  and four or more radar units  476 . Further, sensors  470  may be configured to provide sensor data to components of autonomous vehicle controller  447  and to elements of autonomous vehicle service platform  401 . As shown in diagram  400 , autonomous vehicle controller  447  includes a planner  464 , a motion controller  462 , a localizer  468 , a perception engine  466 , and a local map generator  440 . Note that elements depicted in diagram  400  of  FIG. 4  may include structures and/or functions as similarly-named elements described in connection to one or more other drawings. 
     Localizer  468  is configured to localize autonomous vehicle (i.e., determine a local pose) relative to reference data, which may include map data, route data (e.g., road network data, such as RNOF-like data), and the like. In some cases, localizer  468  is configured to identify, for example, a point in space that may represent a location of autonomous vehicle  430  relative to features of a representation of an environment. Localizer  468  is shown to include a sensor data integrator  469 , which may be configured to integrate multiple subsets of sensor data (e.g., of different sensor modalities) to reduce uncertainties related to each individual type of sensor. According to some examples, sensor data integrator  469  is configured to fuse sensor data (e.g., LIDAR data, camera data, radar data, etc.) to form integrated sensor data values for determining a local pose. According to some examples, localizer  468  retrieves reference data originating from a reference data repository  405 , which includes a map data repository  405   a  for storing 2D map data, 3D map data, 4D map data, and the like. Localizer  468  may be configured to identify at least a subset of features in the environment to match against map data to identify, or otherwise confirm, a pose of autonomous vehicle  430 . According to some examples, localizer  468  may be configured to identify any amount of features in an environment, such that a set of features can one or more features, or all features. In a specific example, any amount of LIDAR data (e.g., most or substantially all LIDAR data) may be compared against data representing a map for purposes of localization. Generally, non-matched objects resulting from the comparison of the environment features and map data may be a dynamic object, such as a vehicle, bicyclist, pedestrian, etc. Note that detection of dynamic objects, including obstacles, may be performed with or without map data. In particular, dynamic objects may be detected and tracked independently of map data (i.e., in the absence of map data). In some instances, 2D map data and 3D map data may be viewed as “global map data” or map data that has been validated at a point in time by autonomous vehicle service platform  401 . As map data in map data repository  405   a  may be updated and/or validated periodically, a deviation may exist between the map data and an actual environment in which the autonomous vehicle is positioned. Therefore, localizer  468  may retrieve locally-derived map data generated by local map generator  440  to enhance localization. Local map generator  440  is configured to generate local map data in real-time or near real-time. Optionally, local map generator  440  may receive static and dynamic object map data to enhance the accuracy of locally generated maps by, for example, disregarding dynamic objects in localization. According to at least some embodiments, local map generator  440  may be integrated with, or formed as part of, localizer  468 . In at least one case, local map generator  440 , either individually or in collaboration with localizer  468 , may be configured to generate map and/or reference data based on simultaneous localization and mapping (“SLAM”) or the like. Note that localizer  468  may implement a “hybrid” approach to using map data, whereby logic in localizer  468  may be configured to select various amounts of map data from either map data repository  405   a  or local map data from local map generator  440 , depending on the degrees of reliability of each source of map data. Therefore, localizer  468  may still use out-of-date map data in view of locally-generated map data. 
     Perception engine  466  is configured to, for example, assist planner  464  in planning routes and generating trajectories by identifying objects of interest in a surrounding environment in which autonomous vehicle  430  is transiting. Further, probabilities may be associated with each of the object of interest, whereby a probability may represent a likelihood that an object of interest may be a threat to safe travel (e.g., a fast-moving motorcycle may require enhanced tracking rather than a person sitting at a bus stop bench while reading a newspaper). As shown, perception engine  466  includes an object detector  442  and an object classifier  444 . Object detector  442  is configured to distinguish objects relative to other features in the environment, and object classifier  444  may be configured to classify objects as either dynamic or static objects and track the locations of the dynamic and the static objects relative to autonomous vehicle  430  for planning purposes. Further, perception engine  466  may be configured to assign an identifier to a static or dynamic object that specifies whether the object is (or has the potential to become) an obstacle that may impact path planning at planner  464 . Although not shown in  FIG. 4 , note that perception engine  466  may also perform other perception-related functions, such as segmentation and tracking, examples of which are described below. 
     Planner  464  is configured to generate a number of candidate trajectories for accomplishing a goal to reaching a destination via a number of paths or routes that are available. Trajectory evaluator  465  is configured to evaluate candidate trajectories and identify which subsets of candidate trajectories are associated with higher degrees of confidence levels of providing collision-free paths to the destination. As such, trajectory evaluator  465  can select an optimal trajectory based on relevant criteria for causing commands to generate control signals for vehicle components  450  (e.g., actuators or other mechanisms). Note that the relevant criteria may include any number of factors that define optimal trajectories, the selection of which need not be limited to reducing collisions. For example, the selection of trajectories may be made to optimize user experience (e.g., user comfort) as well as collision-free trajectories that comply with traffic regulations and laws. User experience may be optimized by moderating accelerations in various linear and angular directions (e.g., to reduce jerking-like travel or other unpleasant motion). In some cases, at least a portion of the relevant criteria can specify which of the other criteria to override or supersede, while maintain optimized, collision-free travel. For example, legal restrictions may be temporarily lifted or deemphasized when generating trajectories in limited situations (e.g., crossing double yellow lines to go around a cyclist or travelling at higher speeds than the posted speed limit to match traffic flows). As such, the control signals are configured to cause propulsion and directional changes at the drivetrain and/or wheels. In this example, motion controller  462  is configured to transform commands into control signals (e.g., velocity, wheel angles, etc.) for controlling the mobility of autonomous vehicle  430 . In the event that trajectory evaluator  465  has insufficient information to ensure a confidence level high enough to provide collision-free, optimized travel, planner  464  can generate a request to teleoperator  404  for teleoperator support. 
     Autonomous vehicle service platform  401  includes teleoperator  404  (e.g., a teleoperator computing device), reference data repository  405 , a map updater  406 , a vehicle data controller  408 , a calibrator  409 , and an off-line object classifier  410 . Note that each element of autonomous vehicle service platform  401  may be independently located or distributed and in communication with other elements in autonomous vehicle service platform  401 . Further, element of autonomous vehicle service platform  401  may independently communicate with the autonomous vehicle  430  via the communication layer  402 . Map updater  406  is configured to receive map data (e.g., from local map generator  440 , sensors  460 , or any other component of autonomous vehicle controller  447 ), and is further configured to detect deviations, for example, of map data in map data repository  405   a  from a locally-generated map. Vehicle data controller  408  can cause 2D map updater  406  to update reference data within repository  405  and facilitate updates to 2D, 3D, and/or 4D map data. In some cases, vehicle data controller  408  can control the rate at which local map data is received into autonomous vehicle service platform  408  as well as the frequency at which map updater  406  performs updating of the map data. 
     Calibrator  409  is configured to perform calibration of various sensors of the same or different types. Calibrator  409  may be configured to determine the relative poses of the sensors (e.g., in Cartesian space (x, y, z)) and orientations of the sensors (e.g., roll, pitch and yaw). The pose and orientation of a sensor, such a camera, LIDAR sensor, radar sensor, etc., may be calibrated relative to other sensors, as well as globally relative to the vehicle&#39;s reference frame. Off-line self-calibration can also calibrate or estimate other parameters, such as vehicle inertial tensor, wheel base, wheel radius or surface road friction. Calibration can also be done online to detect parameter change, according to some examples. Note, too, that calibration by calibrator  409  may include intrinsic parameters of the sensors (e.g., optical distortion, beam angles, etc.) and extrinsic parameters. In some cases, calibrator  409  may be performed by maximizing a correlation between depth discontinuities in 3D laser data and edges of image data, as an example. Off-line object classification  410  is configured to receive data, such as sensor data, from sensors  470  or any other component of autonomous vehicle controller  447 . According to some embodiments, an off-line classification pipeline of off-line object classification  410  may be configured to pre-collect and annotate objects (e.g., manually by a human and/or automatically using an offline labeling algorithm), and may further be configured to train an online classifier (e.g., object classifier  444 ), which can provide real-time classification of object types during online autonomous operation. 
       FIG. 5  is an example of a flow diagram to control an autonomous vehicle, according to some embodiments. At  502 , flow  500  begins when sensor data originating from sensors of multiple modalities at an autonomous vehicle is received, for example, by an autonomous vehicle controller. One or more subsets of sensor data may be integrated for generating fused data to improve, for example, estimates. In some examples, a sensor stream of one or more sensors (e.g., of same or different modalities) may be fused to form fused sensor data at  504 . In some examples, subsets of LIDAR sensor data and camera sensor data may be fused at  504  to facilitate localization. At  506 , data representing objects based on the least two subsets of sensor data may be derived at a processor. For example, data identifying static objects or dynamic objects may be derived (e.g., at a perception engine) from at least LIDAR and camera data. At  508 , a detected object is determined to affect a planned path, and a subset of trajectories are evaluated (e.g., at a planner) responsive to the detected object at  510 . A confidence level is determined at  512  to exceed a range of acceptable confidence levels associated with normative operation of an autonomous vehicle. Therefore, in this case, a confidence level may be such that a certainty of selecting an optimized path is less likely, whereby an optimized path may be determined as a function of the probability of facilitating collision-free travel, complying with traffic laws, providing a comfortable user experience (e.g., comfortable ride), and/or generating candidate trajectories on any other factor. As such, a request for an alternate path may be transmitted to a teleoperator computing device at  514 . Thereafter, the teleoperator computing device may provide a planner with an optimal trajectory over which an autonomous vehicle made travel. In situations, the vehicle may also determine that executing a safe-stop maneuver is the best course of action (e.g., safely and automatically causing an autonomous vehicle to a stop at a location of relatively low probabilities of danger). Note that the order depicted in this and other flow charts herein are not intended to imply a requirement to linearly perform various functions as each portion of a flow chart may be performed serially or in parallel with anyone or more other portions of the flow chart, as well as independent or dependent on other portions of the flow chart. 
       FIG. 6  is a diagram depicting an example of an architecture for an autonomous vehicle controller, according to some embodiments. Diagram  600  depicts a number of processes including a motion controller process  662 , a planner processor  664 , a perception process  666 , a mapping process  640 , and a localization process  668 , some of which may generate or receive data relative to other processes. Other processes, such as such as processes  670  and  650  may facilitate interactions with one or more mechanical components of an autonomous vehicle. For example, perception process  666 , mapping process  640 , and localization process  668  are configured to receive sensor data from sensors  670 , whereas planner process  664  and perception process  666  are configured to receive guidance data  606 , which may include route data, such as road network data. Further to diagram  600 , localization process  668  is configured to receive map data  605   a  (i.e., 2D map data), map data  605   b  (i.e., 3D map data), and local map data  642 , among other types of map data. For example, localization process  668  may also receive other forms of map data, such as 4D map data, which may include, for example, an epoch determination. Localization process  668  is configured to generate local position data  641  representing a local pose. Local position data  641  is provided to motion controller process  662 , planner process  664 , and perception process  666 . Perception process  666  is configured to generate static and dynamic object map data  667 , which, in turn, may be transmitted to planner process  664 . In some examples, static and dynamic object map data  667  may be transmitted with other data, such as semantic classification information and predicted object behavior. Planner process  664  is configured to generate trajectories data  665 , which describes a number of trajectories generated by planner  664 . Motion controller process uses trajectories data  665  to generate low-level commands or control signals for application to actuators  650  to cause changes in steering angles and/or velocity. 
       FIG. 7  is a diagram depicting an example of an autonomous vehicle service platform implementing redundant communication channels to maintain reliable communications with a fleet of autonomous vehicles, according to some embodiments. Diagram  700  depicts an autonomous vehicle service platform  701  including a reference data generator  705 , a vehicle data controller  702 , an autonomous vehicle fleet manager  703 , a teleoperator manager  707 , a simulator  740 , and a policy manager  742 . Reference data generator  705  is configured to generate and modify map data and route data (e.g., RNDF data). Further, reference data generator  705  may be configured to access 2D maps in 2D map data repository  720 , access 3D maps in 3D map data repository  722 , and access route data in route data repository  724 . Other map representation data and repositories may be implemented in some examples, such as 4D map data including Epoch Determination. Vehicle data controller  702  may be configured to perform a variety of operations. For example, vehicle data controller  702  may be configured to change a rate that data is exchanged between a fleet of autonomous vehicles and platform  701  based on quality levels of communication over channels  770 . During bandwidth-constrained periods, for example, data communications may be prioritized such that teleoperation requests from autonomous vehicle  730  are prioritized highly to ensure delivery. Further, variable levels of data abstraction may be transmitted per vehicle over channels  770 , depending on bandwidth available for a particular channel. For example, in the presence of a robust network connection, full LIDAR data (e.g., substantially all LIDAR data, but also may be less) may be transmitted, whereas in the presence of a degraded or low-speed connection, simpler or more abstract depictions of the data may be transmitted (e.g., bounding boxes with associated metadata, etc.). Autonomous vehicle fleet manager  703  is configured to coordinate the dispatching of autonomous vehicles  730  to optimize multiple variables, including an efficient use of battery power, times of travel, whether or not an air-conditioning unit in an autonomous vehicle  730  may be used during low charge states of a battery, etc., any or all of which may be monitored in view of optimizing cost functions associated with operating an autonomous vehicle service. An algorithm may be implemented to analyze a variety of variables with which to minimize costs or times of travel for a fleet of autonomous vehicles. Further, autonomous vehicle fleet manager  703  maintains an inventory of autonomous vehicles as well as parts for accommodating a service schedule in view of maximizing up-time of the fleet. 
     Teleoperator manager  707  is configured to manage a number of teleoperator computing devices  704  with which teleoperators  708  provide input. Simulator  740  is configured to simulate operation of one or more autonomous vehicles  730 , as well as the interactions between teleoperator manager  707  and an autonomous vehicle  730 . Simulator  740  may also simulate operation of a number of sensors (including the introduction of simulated noise) disposed in autonomous vehicle  730 . Further, an environment, such as a city, may be simulated such that a simulated autonomous vehicle can be introduced to the synthetic environment, whereby simulated sensors may receive simulated sensor data, such as simulated laser returns. Simulator  740  may provide other functions as well, including validating software updates and/or 2D map data. Policy manager  742  is configured to maintain data representing policies or rules by which an autonomous vehicle ought to behave in view of a variety of conditions or events that an autonomous vehicle encounters while traveling in a network of roadways. In some cases, updated policies and/or rules may be simulated in simulator  740  to confirm safe operation of a fleet of autonomous vehicles in view of changes to a policy. Some of the above-described elements of autonomous vehicle service platform  701  are further described hereinafter. 
     Communication channels  770  are configured to provide networked communication links among a fleet of autonomous vehicles  730  and autonomous vehicle service platform  701 . For example, communication channel  770  includes a number of different types of networks  771 ,  772 ,  773 , and  774 , with corresponding subnetworks (e.g.,  771   a  to  771   n ), to ensure a certain level of redundancy for operating an autonomous vehicle service reliably. For example, the different types of networks in communication channels  770  may include different cellular network providers, different types of data networks, etc., to ensure sufficient bandwidth in the event of reduced or lost communications due to outages in one or more networks  771 ,  772 ,  773 , and  774 . 
       FIG. 8  is a diagram depicting an example of a messaging application configured to exchange data among various applications, according to some embodiments. Diagram  800  depicts an teleoperator application  801  disposed in a teleoperator manager, and an autonomous vehicle application  830  disposed in an autonomous vehicle, whereby teleoperator applications  801  and autonomous vehicle application  830  exchange message data via a protocol that facilitates communications over a variety of networks, such as network  871 ,  872 , and other networks  873 . According to some examples, the communication protocol is a middleware protocol implemented as a Data Distribution Service™ having a specification maintained by the Object Management Group consortium. In accordance with the communications protocol, teleoperator application  801  and autonomous vehicle application  830  may include a message router  854  disposed in a message domain, the message router being configured to interface with the teleoperator API  852 . In some examples, message router  854  is a routing service. In some examples, message domain  850   a  in teleoperator application  801  may be identified by a teleoperator identifier, whereas message domain  850   b  be may be identified as a domain associated with a vehicle identifier. Teleoperator API  852  in teleoperator application  801  is configured to interface with teleoperator processes  803   a  to  803   c , whereby teleoperator process  803   b  is associated with an autonomous vehicle identifier  804 , and teleoperator process  803   c  is associated with an event identifier  806  (e.g., an identifier that specifies an intersection that may be problematic for collision-free path planning). Teleoperator API  852  in autonomous vehicle application  830  is configured to interface with an autonomous vehicle operating system  840 , which includes sensing application  842 , a perception application  844 , a localization application  846 , and a control application  848 . In view of the foregoing, the above-described communications protocol may facilitate data exchanges to facilitate teleoperations as described herein. Further, the above-described communications protocol may be adapted to provide secure data exchanges among one or more autonomous vehicles and one or more autonomous vehicle service platforms. For example, message routers  854  may be configured to encrypt and decrypt messages to provide for secured interactions between, for example, a teleoperator process  803  and an autonomous vehicle operation system  840 . 
       FIG. 9  is a diagram depicting types of data for facilitating teleoperations using a communications protocol described in  FIG. 8 , according to some examples. Diagram  900  depicts a teleoperator  908  interfacing with a teleoperator computing device  904  coupled to a teleoperator application  901 , which is configured to exchange data via a data-centric messaging bus  972  implemented in one or more networks  971 . Data-centric messaging bus  972  provides a communication link between teleoperator application  901  and autonomous vehicle application  930 . Teleoperator API  962  of teleoperator application  901  is configured to receive message service configuration data  964  and route data  960 , such as road network data (e.g., RNDF-like data), mission data (e.g., MDFdata), and the like. Similarly, a messaging service bridge  932  is also configured to receive messaging service configuration data  934 . Messaging service configuration data  934  and  964  provide configuration data to configure the messaging service between teleoperator application  901  and autonomous vehicle application  930 . An example of messaging service configuration data  934  and  964  includes quality of service (“QoS”) configuration data implemented to configure a Data Distribution Service™ application. 
     An example of a data exchange for facilitating teleoperations via the communications protocol is described as follows. Consider that obstacle data  920  is generated by a perception system of an autonomous vehicle controller. Further, planner options data  924  is generated by a planner to notify a teleoperator of a subset of candidate trajectories, and position data  926  is generated by the localizer. Obstacle data  920 , planner options data  924 , and position data  926  are transmitted to a messaging service bridge  932 , which, in accordance with message service configuration data  934 , generates telemetry data  940  and query data  942 , both of which are transmitted via data-centric messaging bus  972  into teleoperator application  901  as telemetry data  950  and query data  952 . Teleoperator API  962  receives telemetry data  950  and inquiry data  952 , which, in turn are processed in view of Route data  960  and message service configuration data  964 . The resultant data is subsequently presented to a teleoperator  908  via teleoperator computing device  904  and/or a collaborative display (e.g., a dashboard display visible to a group of collaborating teleoperators  908 ). Teleoperator  908  reviews the candidate trajectory options that are presented on the display of teleoperator computing device  904 , and selects a guided trajectory, which generates command data  982  and query response data  980 , both of which are passed through teleoperator API  962  as query response data  954  and command data  956 . In turn, query response data  954  and command data  956  are transmitted via data-centric messaging bus  972  into autonomous vehicle application  930  as query response data  944  and command data  946 . Messaging service bridge  932  receives query response data  944  and command data  946  and generates teleoperator command data  928 , which is configured to generate a teleoperator-selected trajectory for implementation by a planner. Note that the above-described messaging processes are not intended to be limiting, and other messaging protocols may be implemented as well. 
       FIG. 10  is a diagram illustrating an example of a teleoperator interface with which a teleoperator may influence path planning, according to some embodiments. Diagram  1000  depicts examples of an autonomous vehicle  1030  in communication with an autonomous vehicle service platform  1001 , which includes a teleoperator manager  1007  configured to facilitate teleoperations. In a first example, teleoperator manager  1007  receives data that requires teleoperator  1008  to preemptively view a path of an autonomous vehicle approaching a potential obstacle or an area of low planner confidence levels so that teleoperator  1008  may be able to address an issue in advance. To illustrate, consider that an intersection that an autonomous vehicle is approaching may be tagged as being problematic. As such, user interface  1010  displays a representation  1014  of a corresponding autonomous vehicle  1030  transiting along a path  1012 , which has been predicted by a number of trajectories generated by a planner. Also displayed are other vehicles  1011  and dynamic objects  1013 , such as pedestrians, that may cause sufficient confusion at the planner, thereby requiring teleoperation support. User interface  1010  also presents to teleoperator  1008  a current velocity  1022 , a speed limit  1024 , and an amount of charge  1026  presently in the batteries. According to some examples, user interface  1010  may display other data, such as sensor data as acquired from autonomous vehicle  1030 . In a second example, consider that planner  1064  has generated a number of trajectories that are coextensive with a planner-generated path  1044  regardless of a detected unidentified object  1046 . Planner  1064  may also generate a subset of candidate trajectories  1040 , but in this example, the planner is unable to proceed given present confidence levels. If planner  1064  fails to determine an alternative path, a teleoperation request may be transmitted. In this case, a teleoperator may select one of candidate trajectories  1040  to facilitate travel by autonomous vehicle  1030  that is consistent with teleoperator-based path  1042 . 
       FIG. 11  is a diagram depicting an example of a planner configured to invoke teleoperations, according to some examples. Diagram  1100  depicts a planner  1164  including a topography manager  1110 , a route manager  1112 , a path generator  1114 , a trajectory evaluator  1120 , and a trajectory tracker  1128 . Topography manager  1110  is configured to receive map data, such as 3D map data or other like map data that specifies topographic features. Topography manager  1110  is further configured to identify candidate paths based on topographic-related features on a path to a destination. According to various examples, topography manager  1110  receives 3D maps generated by sensors associated with one or more autonomous vehicles in the fleet. Route manager  1112  is configured to receive environmental data  1103 , which may include traffic-related information associated with one or more routes that may be selected as a path to the destination. Path generator  1114  receives data from topography manager  1110  and route manager  1112 , and generates one or more paths or path segments suitable to direct autonomous vehicle toward a destination. Data representing one or more paths or path segments is transmitted into trajectory evaluator  1120 . 
     Trajectory evaluator  1120  includes a state and event manager  1122 , which, in turn, may include a confidence level generator  1123 . Trajectory evaluator  1120  further includes a guided trajectory generator  1126  and a trajectory generator  1124 . Further, planner  1164  is configured to receive policy data  1130 , perception engine data  30   1132 , and localizer data  1134 . 
     Policy data  1130  may include criteria with which planner  1164  uses to determine a path that has a sufficient confidence level with which to generate trajectories, according to some examples. Examples of policy data  1130  include policies that specify that trajectory generation is bounded by stand-off distances to external objects (e.g., maintaining a safety buffer of 3 feet from a cyclist, as possible), or policies that require that trajectories must not cross a center double yellow line, or policies that require trajectories to be limited to a single lane in a 4-lane roadway (e.g., based on past events, such as typically congregating at a lane closest to a bus stop), and any other similar criteria specified by policies. Perception engine data  1132  includes maps of locations of static objects and dynamic objects of interest, and localizer data  1134  includes at least a local pose or position. 
     State and event manager  1122  may be configured to probabilistically determine a state of operation for an autonomous vehicle. For example, a first state of operation (i.e., “normative operation”) may describe a situation in which trajectories are collision-free, whereas a second state of operation (i.e., “non-normative operation”) may describe another situation in which the confidence level associated with possible trajectories are insufficient to guarantee collision-free travel. According to some examples, state and event manager  1122  is configured to use perception data  1132  to determine a state of autonomous vehicle that is either normative or non-normative. Confidence level generator  1123  may be configured to analyze perception data  1132  to determine a state for the autonomous vehicle. For example, confidence level generator  1123  may use semantic information associated with static and dynamic objects, as well as associated probabilistic estimations, to enhance a degree of certainty that planner  1164  is determining safe course of action. For example, planner  1164  may use perception engine data  1132  that specifies a probability that an object is either a person or not a person to determine whether planner  1164  is operating safely (e.g., planner  1164  may receive a degree of certainty that an object has a 98% probability of being a person, and a probability of 2% that the object is not a person). 
     Upon determining a confidence level (e.g., based on statistics and 30 probabilistic determinations) is below a threshold required for predicted safe operation, relatively low confidence level (e.g., single probability score) may trigger planner  1164  to transmit a request  1135  for teleoperation support to autonomous vehicle service platform  1101 . In some cases, telemetry data and a set of candidate trajectories may accompany the request. Examples of telemetry data include sensor data, localization data, perception data, and the like. A teleoperator  1108  may transmit via teleoperator computing device  1104  a selected trajectory  1137  to guided trajectory generator  1126 . As such, selected trajectory  1137  is a trajectory formed with guidance from a teleoperator. Upon confirming there is no change in the state (e.g., a non-normative state is pending), guided trajectory generator  1126  passes data to trajectory generator  1124 , which, in turn, causes trajectory tracker  1128 , as a trajectory tracking controller, to use the teleop-specified trajectory for generating control signals  1170  (e.g., steering angles, velocity, etc.). Note that planner  1164  may trigger transmission of a request  1135  for teleoperation support prior to a state transitioning to a non-normative state. In particular, an autonomous vehicle controller and/or its components can predict that a distant obstacle may be problematic and preemptively cause planner  1164  to invoke teleoperations prior to the autonomous vehicle reaching the obstacle. Otherwise, the autonomous vehicle may cause a delay by transitioning to a safe state upon encountering the obstacle or scenario (e.g., pulling over and off the roadway). In another example, teleoperations may be automatically invoked prior to an autonomous vehicle approaching a particular location that is known to be difficult to navigate. This determination may optionally take into consideration other factors, including the time of day, the position of the sun, if such situation is likely to cause a disturbance to the reliability of sensor readings, and traffic or accident data derived from a variety of sources. 
       FIG. 12  is an example of a flow diagram configured to control an autonomous vehicle, according to some embodiments. At  1202 , flow  1200  begins. Data representing a subset of objects that are received at a planner in an autonomous vehicle, the subset of objects including at least one object associated with data representing a degree of certainty for a classification type. For example, perception engine data may include metadata associated with objects, whereby the metadata specifies a degree of certainty associated with a specific classification type. For instance, a dynamic object may be classified as a “young pedestrian” with an 85% confidence level of being correct. At  1204 , localizer data may be received (e.g., at a planner). The localizer data may include map data that is generated locally within the autonomous vehicle. The local map data may specify a degree of certainty (including a degree of uncertainty) that an event at a geographic region may occur. An event may be a condition or situation affecting operation, or potentially affecting operation, of an autonomous vehicle. The events may be internal (e.g., failed or impaired sensor) to an autonomous vehicle, or external (e.g., roadway obstruction). Examples of events are described herein, such as in  FIG. 2  as well as in other figures and passages. A path coextensive with the geographic region of interest may be determined at  1206 . For example, consider that the event is the positioning of the sun in the sky at a time of day in which the intensity of sunlight impairs the vision of drivers during rush hour traffic. As such, it is expected or predicted that traffic may slow down responsive to the bright sunlight. Accordingly, a planner may preemptively invoke teleoperations if an alternate path to avoid the event is less likely. At  1208 , a local position is determined at a planner based on local pose data. At  1210 , a state of operation of an autonomous vehicle may be determined (e.g., probabilistically), for example, based on a degree of certainty for a classification type and a degree of certainty of the event, which is may be based on any number of factors, such as speed, position, and other state information. To illustrate, consider an example in which a young pedestrian is detected by the autonomous vehicle during the event in which other drivers&#39; vision likely will be impaired by the sun, thereby causing an unsafe situation for the young pedestrian. Therefore, a relatively unsafe situation can be detected as a probabilistic event that may be likely to occur (i.e., an unsafe situation for which teleoperations may be invoked). At  1212 , a likelihood that the state of operation is in a normative state is determined, and based on the determination, a message is transmitted to a teleoperator computing device requesting teleoperations to preempt a transition to a next state of operation (e.g., preempt transition from a normative to non-normative state of operation, such as an unsafe state of operation). 
       FIG. 13  depicts an example in which a planner may generate a 30 trajectory, according to some examples. Diagram  1300  includes a trajectory evaluator  1320  and a trajectory generator  1324 . Trajectory evaluator  1320  includes a confidence level generator  1322  and a teleoperator query messenger  1329 . As shown, trajectory evaluator  1320  is coupled to a perception engine  1366  to receive static map data  1301 , and current and predicted object state data  1303 . Trajectory evaluator  1320  also receives local pose data  1305  from localizer  1368  and plan data  1307  from a global planner  1369 . In one state of operation (e.g., non-normative), confidence level generator  1322  receives static map data  1301  and current and predicted object state data  1303 . Based on this data, confidence level generator  1322  may determine that detected trajectories are associated with unacceptable confidence level values. As such, confidence level generator  1322  transmits detected trajectory data  1309  (e.g., data including candidate trajectories) to notify a teleoperator via teleoperator query messenger  1329 , which, in turn, transmits a request  1370  for teleoperator assistance. 
     In another state of operation (e.g., a normative state), static map data  1301 , current and predicted object state data  1303 , local pose data  1305 , and plan data  1307  (e.g., global plan data) are received into trajectory calculator  1325 , which is configured to calculate (e.g., iteratively) trajectories to determine an optimal one or more paths. Next, at least one path is selected and is transmitted as selected path data  1311 . According to some embodiments, trajectory calculator  1325  is configured to implement re-planning of trajectories as an example. Nominal driving trajectory generator  1327  is configured to generate trajectories in a refined approach, such as by generating trajectories based on receding horizon control techniques. Nominal driving trajectory generator  1327  subsequently may transmit nominal driving trajectory path data  1372  to, for example, a trajectory tracker or a vehicle controller to implement physical changes in steering, acceleration, and other components. 
       FIG. 14  is a diagram depicting another example of an autonomous vehicle service platform, according to some embodiments. Diagram  1400  depicts an autonomous vehicle service platform  1401  including a teleoperator manager  1407  that is configured to manage interactions and/or communications among teleoperators  1408 , teleoperator computing devices  1404 , and other components of autonomous vehicle service platform  1401 . Further to diagram  1400 , autonomous vehicle service platform  1401  includes a simulator  1440 , a repository  1441 , a policy manager  1442 , a reference data updater  1438 , a 20 map data repository  1420 , a 3D map data repository  1422 , and a route data repository  1424 . Other map data, such as  40  map data (e.g., using epoch determination), may be implemented and stored in a repository (not shown). 
     Teleoperator action recommendation controller  1412  includes logic configured to receive and/or control a teleoperation service request via autonomous vehicle (“AV”) planner data  1472 , which can include requests for teleoperator assistance as well as telemetry data and other data. As such, planner data  1472  may include recommended candidate trajectories or paths from which a teleoperator  1408  via teleoperator computing device  1404  may select. According to some examples, teleoperator action recommendation controller  1412  may be configured to access other sources of recommended candidate trajectories from which to select an optimum trajectory. For example, candidate trajectories contained in autonomous vehicle planner data  1472  may, in parallel, be introduced into simulator  1440 , which is configured to simulate an event or condition being experienced by an autonomous vehicle requesting teleoperator assistance. Simulator  1440  can access map data and other data necessary for performing a simulation on the set of candidate trajectories, whereby simulator  1440  need not exhaustively reiterate simulations to confirm sufficiency. Rather, simulator  1440  may provide either confirm the appropriateness of the candidate trajectories, or may otherwise alert a teleoperator to be cautious in their selection. 
     Teleoperator interaction capture analyzer  1416  may be configured to capture numerous amounts of teleoperator transactions or interactions for storage in repository  1441 , which, for example, may accumulate data relating to a number of teleoperator transactions for analysis and generation of policies, at least in some cases. According to some embodiments, repository  1441  may also be configured to store policy data for access by policy manager  1442 . Further, teleoperator interaction capture analyzer  1416  may apply machine learning techniques to empirically determine how best to respond to events or conditions causing requests for teleoperation assistance. In some cases, policy manager  1442  may be configured to update a particular policy or generate a new policy responsive to analyzing the large set of teleoperator interactions (e.g., subsequent to applying machine learning techniques). Policy manager  1442  manages policies that may be viewed as rules or guidelines with which an autonomous vehicle controller and its components operate under to comply with autonomous operations of a vehicle. In some cases, a modified or updated policy may be applied to simulator  1440  to confirm the efficacy of permanently releasing or implementing such policy changes. 
     Simulator interface controller  1414  is configured to provide an interface between simulator  1440  and teleoperator computing devices  1404 . For example, consider that sensor data from a fleet of autonomous vehicles is applied to reference data updater  1438  via autonomous (“AV”) fleet data  1470 , whereby reference data updater  1438  is configured to generate updated map and route data  1439 . In some implementations, updated map and route data  1439  may be preliminarily released as an update to data in map data repositories  1420  and  1422 , or as an update to data in route data repository  1424 . In this case, such data may be tagged as being a “beta version” in which a lower threshold for requesting teleoperator service may be implemented when, for example, a map tile including preliminarily updated information is used by an autonomous vehicle. Further, updated map and route data  1439  may be introduced to simulator  1440  for validating the updated map data. Upon full release (e.g., at the close of beta testing), the previously lowered threshold for requesting a teleoperator service related to map tiles is canceled. User interface graphics controller  1410  provides rich graphics to teleoperators  1408 , whereby a fleet of autonomous vehicles may be simulated within simulator  1440  and may be accessed via teleoperator computing device  1404  as if the simulated fleet of autonomous vehicles were real. 
       FIG. 15  is an example of a flow diagram to control an autonomous vehicle, according to some embodiments. At  1502 , flow  1500  begins. Message data may be received at a teleoperator computing device for managing a fleet of autonomous vehicles. The message data may indicate event attributes associated with a non-normative state of operation in the context of a planned path for an autonomous vehicle. For example, an event may be characterized as a particular intersection that becomes problematic due to, for example, a large number of pedestrians, hurriedly crossing the street against a traffic light. The event attributes describe the characteristics of the event, such as, for example, the number of people crossing the street, the traffic delays resulting from an increased number of pedestrians, etc. At  1504 , a teleoperation repository may be accessed to retrieve a first subset of recommendations based on simulated operations of aggregated data associated with a group of autonomous vehicles. In this case, a simulator may be a source of recommendations with which a teleoperator may implement. Further, the teleoperation repository may also be accessed to retrieve a second subset of recommendations based on an aggregation of teleoperator interactions responsive to similar event attributes. In particular, a teleoperator interaction capture analyzer may apply machine learning techniques to empirically determine how best to respond to events having similar attributes based on previous requests for teleoperation assistance. At  1506 , the first subset and the second subset of recommendations are combined to form a set of recommended courses of action for the autonomous vehicle. At  1508 , representations of the set of recommended courses of actions may be presented visually on a display of a teleoperator computing device. At  1510 , data signals representing a selection (e.g., by teleoperator) of a recommended course of action may be detected. 
       FIG. 16  is a diagram of an example of an autonomous vehicle fleet manager implementing a fleet optimization manager, according to some examples. Diagram  1600  depicts an autonomous vehicle fleet manager that is configured to manage a fleet of autonomous vehicles  1630  transiting within a road network  1650 . Autonomous vehicle fleet manager  1603  is coupled to a teleoperator  1608  via a teleoperator computing device  1604 , and is also coupled to a fleet management data repository  1646 . Autonomous vehicle fleet manager  1603  is configured to receive policy data  1602  and environmental data  1606 , as well as other data. Further to diagram  1600 , fleet optimization manager  1620  is shown to include a transit request processor  1631 , which, in turn, includes a fleet data extractor  1632  and an autonomous vehicle dispatch optimization calculator  1634 . Transit request processor  1631  is configured to process transit requests, such as from a user  1688  who is requesting autonomous vehicle service. Fleet data extractor  1632  is configured to extract data relating to autonomous vehicles in the fleet. Data associated with each autonomous vehicle is stored in repository  1646 . For example, data for each vehicle may describe maintenance issues, scheduled service calls, daily usage, battery charge and discharge rates, and any other data, which may be updated in real-time, may be used for purposes of optimizing a fleet of autonomous vehicles to minimize downtime. Autonomous vehicle dispatch optimization calculator  1634  is configured to analyze the extracted data and calculate optimized usage of the fleet so as to ensure that the next vehicle dispatched, such as from station  1652 , provides for the least travel times and/or costs-in the aggregate-for the autonomous vehicle service. 
     Fleet optimization manager  1620  is shown to include a hybrid autonomous vehicle/non-autonomous vehicle processor  1640 , which, in turn, includes an AV/non-AV optimization calculator  1642  and a non-AV selector  1644 . According to some examples, hybrid autonomous vehicle/non-autonomous vehicle processor  1640  is configured to manage a hybrid fleet of autonomous vehicles and human-driven vehicles (e.g., as independent contractors). As such, autonomous vehicle service may employ non-autonomous vehicles to meet excess demand, or in areas, such as non-AV service region  1690 , that may be beyond a geo-fence or in areas of poor communication coverage. AV/non-AV optimization calculator  1642  is configured to optimize usage of the fleet of autonomous and to invite non-AV drivers into the transportation service (e.g., with minimal or no detriment to the autonomous vehicle service). Non-AV selector  1644  includes logic for selecting a number of non-AV drivers to assist based on calculations derived by AV/non-AV optimization calculator  1642 . 
       FIG. 17  is an example of a flow diagram to manage a fleet of autonomous vehicles, according to some embodiments. At  1702 , flow  1700  begins. At  1702 , policy data is received. The policy data may include parameters that define how best apply to select an autonomous vehicle for servicing a transit request. At  1704 , fleet management data from a repository may be extracted. The fleet management data includes subsets of data for a pool of autonomous vehicles (e.g., the data describes the readiness of vehicles to service a transportation request). At  1706 , data representing a transit request is received. For exemplary purposes, the transit request could be for transportation from a first geographic location to a second geographic location. At  1708 , attributes based on the policy data are calculated to determine a subset of autonomous vehicles that are available to service the request. For example, attributes may include a battery charge level and time until next scheduled maintenance. At  1710 , an autonomous vehicle is selected as transportation from the first geographic location to the second geographic location, and data is generated to dispatch the autonomous vehicle to a third geographic location associated with the origination of the transit request. 
       FIG. 18  is a diagram illustrating an autonomous vehicle fleet manager implementing an autonomous vehicle communications link manager, according to some embodiments. Diagram  1800  depicts an autonomous vehicle fleet manager that is configured to manage a fleet of autonomous vehicles  1830  transiting within a road network  1850  that coincides with a communication outage at an area identified as “reduced communication region”  1880 . Autonomous vehicle fleet manager  1803  is coupled to a teleoperator  1808  via a teleoperator computing device  1804 . Autonomous vehicle fleet manager  1803  is configured to receive polity data  1802  and environmental data  1806 , as well as other data. Further to diagram  1800 , an autonomous vehicle communications link manager  1820  is shown to include an environment event detector  1831 , a policy adaption determinator  1832 , and a transit request processor  1834 . Environment event detector  1831  is configured to receive environmental data  1806  specifying a change within the environment in which autonomous vehicle service is implemented. For example, environmental data  1806  may specify that region  1880  has degraded communication services, which may affect the autonomous vehicle service. Policy adaption determinator  1832  may specify parameters with which to apply when receiving transit requests during such an event (e.g., during a loss of communications). Transit request processor  1834  is configured to process transit requests in view of the degraded communications. In this example, a user  1888  is requesting autonomous vehicle service. Further, transit request processor  1834  includes logic to apply an adapted policy for modifying the way autonomous vehicles are dispatched so to avoid complications due to poor communications. 
     Communication event detector  1840  includes a policy download manager  1842  and communications-configured (“COMM-configured”) AV dispatcher  1844 . Policy download manager  1842  is configured to provide autonomous vehicles  1830  an updated policy in view of reduced communications region  1880 , whereby the updated policy may specify routes to quickly exit region  1880  if an autonomous vehicle enters that region. For example, autonomous vehicle  1864  may receive an updated policy moments before driving into region  1880 . Upon loss of communications, autonomous vehicle  1864  implements the updated policy and selects route  1866  to drive out of region  1880  quickly. COMM-configured AV dispatcher  1844  may be configured to identify points  1865  at which to park autonomous vehicles that are configured as relays to establishing a peer-to-peer network over region  1880 . As such, COMM-configured AV dispatcher  1844  is configured to dispatch autonomous vehicles  1862  (without passengers) to park at locations  1865  for the purposes of operating as communication towers in a peer-to-peer ad hoc network. 
       FIG. 19  is an example of a flow diagram to determine actions for autonomous vehicles during an event, such as degraded or lost communications, according to some embodiments. At  1901 , flow  1900  begins. Policy data is received, whereby the policy data defines parameters with which to apply to transit requests in a geographical region during an event. At  1902 , one or more of the following actions may be implemented: (1) dispatch a subset of autonomous vehicles to geographic locations in the portion of the geographic location, the subset of autonomous vehicles being configured to either park at specific geographic locations and each serve as a static communication relay, or transit in a geographic region to each serve as a mobile communication relay, (2) implement peer-to-peer communications among a portion of the pool of autonomous vehicles associated with the portion of the geographic region, (3) provide to the autonomous vehicles an event policy that describes a route to egress the portion of the geographic region during an event, (4) invoke teleoperations, and (5) recalculate paths so as to avoid the geographic portion. Subsequent to implementing the action, the fleet of autonomous vehicles is monitored at  1914 . 
       FIG. 20  is a diagram depicting an example of a localizer, according to some embodiments. Diagram  2000  includes a localizer  2068  configured to receive sensor data from sensors  2070 , such as LIDAR data  2072 , camera data  2074 , radar data  2076 , and other data  2078 . Further, localizer  2068  is configured to receive reference data 3D  2020 , such as 2D map data  2022 , 3D map data  2024 , and 3D local map data. According to some examples, other map data, such as 4D map data  2025  and semantic map data (not shown), including corresponding data structures and repositories, may also be implemented. Further to diagram  2000 , localizer  2068  includes a positioning system  2010  and a localization system  2012 , both of which are configured to receive sensor data from sensors  2070  as well as reference data  2020 . Localization data integrator  2014  is configured to receive data from positioning system  2010  and data from localization system  2012 , whereby localization data integrator  2014  is configured to integrate or fuse sensor data from multiple sensors to form local pose data  2052 . 
       FIG. 21  is an example of a flow diagram to generate local pose data based on integrated sensor data, according to some embodiments. At  2101 , flow  2100  begins. At  2102 , reference data is received, the reference data including three dimensional map data. In some examples, reference data, such as 3D or 4D map data, may be received via one or more networks. At  2104 , localization data from one or more localization sensors is received and placed into a localization system. At  2106 , positioning data from one or more positioning sensors is received into a positioning system. At  2108 , the localization and positioning data are integrated. At  2110 , the localization data and positioning data are integrated to form local position data specifying a geographic position of an autonomous vehicle. 
       FIG. 22  is a diagram depicting another example of a localizer, according to some embodiments. Diagram  2200  includes a localizer  2268 , which, in turn, includes a localization system  2210  and a relative localization system  2212  to generate positioning-based data  2250  and local location-based data  2251 , respectively. Localization system  2210  includes a projection processor  2254   a  for processing GPS data  2273 , a GPS datum  2211 , and 3D Map data  2222 , among other optional data (e.g., 4D map data). Localization system  2210  also includes an odometry processor  2254   b  to process wheel data  2275  (e.g., wheel speed), vehicle model data  2213  and 3D map data  2222 , among other optional data. Further yet, localization system  2210  includes an integrator processor  2254   c  to process IMU data  2257 , vehicle model data  2215 , and 3D map data  2222 , among other optional data. Similarly, relative localization system  2212   30  includes a LIDAR localization processor  2254   d  for processing LIDAR data  2272 , 2D tile map data  2220 , 3D map data  2222 , and 3D local map data  2223 , among other optional data. Relative localization system  2212  also includes a visual registration processor  2254   e  to process camera data  2274 , 3D map data  2222 , and 3D local map data  2223 , among other optional data. Further yet, relative localization system  2212  includes a radar return processor  2254   f  to process radar data  2276 , 3D map data  2222 , and 3D local map data  2223 , among other optional data. Note that in various examples, other types of sensor data and sensors or processors may be implemented, such as sonar data and the like. 
     Further to diagram  2200 , localization-based data  2250  and relative localization-based data  2251  may be fed into data integrator  2266   a  and localization data integrator  2266 , respectively. Data integrator  2266   a  and localization data integrator  2266  may be configured to fuse corresponding data, whereby localization-based data  2250  may be fused at data integrator  2266   a  prior to being fused with relative localization-based data  2251  at localization data integrator  2266 . According to some embodiments, data integrator  2266   a  is formed as part of localization data integrator  2266 , or is absent. Regardless, a localization-based data  2250  and relative localization-based data  2251  can be both fed into localization data integrator  2266  for purposes of fusing data to generate local position data  2252 . Localization-based data  2250  may include unary-constrained data (and uncertainty values) from projection processor  2254   a , as well as binary-constrained data (and uncertainty values) from odometry processor  2254   b  and integrator processor  2254   c . Relative localization-based data  2251  may include unary-constrained data (and uncertainty values) from localization processor  2254   d  and visual registration processor  2254   e , and optionally from radar return processor  2254   f . According to some embodiments, localization data integrator  2266  may implement non-linear smoothing functionality, such as a Kalman filter (e.g., a gated Kalman filter), a relative bundle adjuster, pose-graph relaxation, particle filter, histogram filter, or the like. 
       FIG. 23  is a diagram depicting an example of a perception engine, according to some embodiments. Diagram  2300  includes a perception engine  2366 , which, in turn, includes a segmentation processor  2310 , an object tracker  2330 , and a classifier  2360 . Further, perception engine  2366  is configured to receive a local position data  2352 , LIDAR data  2372 , camera data  2374 , and radar data  2376 , for example that other sensor data, such as sonar data, may be accessed to provide functionalities of perception engine  2366 . Segmentation processor  2310  is configured to extract ground plane data and/or to segment portions of an image to distinguish objects from each other and from static imagery (e.g., background). In some cases, 3D blobs may be segmented to distinguish each other. In some examples, a blob may refer to a set of features that identify an object in a spatially-reproduced environment and may be composed of elements (e.g., pixels of camera data, points of laser return data, etc.) having similar characteristics, such as intensity and color. In some examples, a blob may also refer to a point cloud (e.g., composed of colored laser return data) or other elements constituting an object. Object tracker  2330  is configured to perform frame-to-frame estimations of motion for blobs, or other segmented image portions. Further, data association is used to associate a blob at one location in a first frame at time, t 1 , to a blob in a different position in a second frame at time, t 2 . In some examples, object tracker  2330  is configured to perform real-time probabilistic tracking of 3D objects, such as blobs. Classifier  2360  is configured to identify an object and to classify that object by classification type (e.g., as a pedestrian, cyclist, etc.) and by energy/activity (e.g. whether the object is dynamic or static), whereby data representing classification is described by a semantic label. According to some embodiments, probabilistic estimations of object categories may be performed, such as classifying an object as a vehicle, bicyclist, pedestrian, etc. with varying confidences per object class. Perception engine  2366  is configured to determine perception engine data  2354 , which may include static object maps and/or dynamic object maps, as well as semantic information so that, for example, a planner may use this information to enhance path planning. According to various examples, one or more of segmentation processor  2310 , object tracker  2330 , and classifier  2360  may apply machine learning techniques to generate perception engine data  2354 . 
       FIG. 24  is an example of a flow chart to generate perception engine data, according to some embodiments. Flow chart  2400  begins at  2402 , at which data representing a local position of an autonomous vehicle is retrieved. At  2404 , localization data from one or more localization sensors is received, and features of an environment in which the autonomous vehicle is disposed are segmented at  2406  to form segmented objects. One or more portions of the segmented object are tracked spatially at  2408  to form at least one tracked object having a motion (e.g., an estimated motion). At  2410 , a tracked object is classified at least as either being a static object or a dynamic object. In some cases, a static object or a dynamic object may be associated with a classification type. At  2412 , data identifying a classified object is generated. For example, the data identifying the classified object may include semantic information. 
       FIG. 25  is an example of a segmentation processor, according to some embodiments. Diagram  2500  depicts a segmentation processor  2510  receiving LIDAR data from one or more LIDARs  2572  and camera image data from one or more cameras  2574 . Local pose data  2552 , LIDAR data, and camera image data are received into meta spin generator  2521 . In some examples, meta spin generator is configured to partition an image based on various attributes (e.g., color, intensity, etc.) into distinguishable regions (e.g., clusters or groups of a point cloud), at least two or more of which may be updated at the same time or about the same time. Meta spin data  2522  is used to perform object segmentation and ground segmentation at segmentation processor  2523 , whereby both meta spin data  2522  and segmentation-related data from segmentation processor  2523  are applied to a scanned differencing processor  2513 . Scanned differencing processor  2513  is configured to predict motion and/or relative velocity of segmented image portions, which can be used to identify dynamic objects at  2517 . Data indicating objects with detected velocity at  2517  are optionally transmitted to the planner to enhance path planning decisions. Additionally, data from scanned differencing processor  2513  may be used to approximate locations of objects to form mapping of such objects (as well as optionally identifying a level of motion). In some examples, an occupancy grid map  2515  may be generated. Data representing an occupancy grid map  2515  may be transmitted to the planner to further enhance path planning decisions (e.g., by reducing uncertainties). Further to diagram  2500 , image camera data from one or more cameras  2574  are used to classify blobs in blob classifier  2520 , which also receives blob data  2524  from segmentation processor  2523 . Segmentation processor  2510  also may receive raw radar returns data  2512  from one or more radars  2576  to perform segmentation at a radar segmentation processor  2514 , which generates radar-related blob data  2516 . Further to  FIG. 25 , segmentation processor  2510  may also receive and/or generate tracked blob data  2518  related to radar data. Blob data  2516 , tracked blob data  2518 , data from blob classifier  2520 , and blob data  2524  may be used to track objects or portions thereof. According to some examples, one or more of the following may be optional: scanned differencing processor  2513 , blob classification  2520 , and data from radar  2576 . 
       FIG. 26A  is a diagram depicting examples of an object tracker and a classifier, according to various embodiments. Object tracker  2630  of diagram  2600  is configured to receive blob data  2516 , tracked blob data  2518 , data from blob classifier  2520 , blob data  2524 , and camera image data from one or more cameras  2676 . Image tracker  2633  is configured to receive camera image data from one or more cameras  2676  to generate tracked image data, which, in turn, may be provided to data association processor  2632 . As shown, data association processor  2632  is configured to receive blob data  2516 , tracked blob data  2518 , data from blob classifier  2520 , blob data  2524 , and track image data from image tracker  2633 , and is further configured to identify one or more associations among the above-described types of data. Data association processor  2632  is configured to track, for example, various blob data from one frame to a next frame to, for example, estimate motion, among other things. Further, data generated by data association processor  2632  may be used by track updater  2634  to update one or more tracks, or tracked objects. In some examples, track updater  2634  may implement a Kalman Filter, or the like, to form updated data for tracked objects, which may be stored online in track database (“DB”)  2636 . Feedback data may be exchanged via path  2699  between data association processor  2632  and track database  2636 . In some examples, image tracker  2633  may be optional and may be excluded. Object tracker  2630  may also use other sensor data, such as radar or sonar, as well as any other types of sensor data, for example. 
       FIG. 26B  is a diagram depicting another example of an object tracker according to at least some examples. Diagram  2601  includes an object tracker  2631  that may include structures and/or functions as similarly-named elements described in connection to one or more other drawings (e.g.,  FIG. 26A ). As shown, object tracker  2631  includes an optional registration portion  2699  that includes a processor  2696  configured to perform object scan registration and data fusion. Processor  2696  is further configured to store the resultant data in 3D object database  2698 . 
     Referring back to  FIG. 26A , diagram  2600  also includes classifier  2660 , which may include a track classification engine  2662  for generating static obstacle data  2672  and dynamic obstacle data  2674 , both of which may be transmitted to the planner for path planning purposes. In at least one example, track classification engine  2662  is configured to determine whether an obstacle is static or dynamic, as well as another classification type for the object (e.g., whether the object is a vehicle, pedestrian, tree, cyclist, dog, cat, paper bag, etc.). Static obstacle data  2672  may be formed as part of an obstacle map (e.g., a 2D occupancy map), and dynamic obstacle data  2674  may be formed to include bounding boxes with data indicative of velocity and classification type. Dynamic obstacle data  2674 , at least in some cases, includes dynamic obstacle map data. 
       FIG. 27  is an example of front-end processor for a perception engine, according to some examples. Diagram  2700  includes a ground segmentation processor  2723   a  for performing ground segmentation, and an over segmentation processor  2723   b  for performing “over-segmentation” according to various examples. Processors  2723   a  and  2723   b  are configured to receive optionally colored LIDAR data  2775 . Over segmentation processor  2723   b  generates data  2710  of a first blob type (e.g., a relatively small blob), which is provided to an aggregation classification and segmentation engine  2712  that generates data  2714  of a second blob type. Data  2714  is provided to data association processor  2732 , which is configured to detect whether data  2714  resides in track database  2736 . A determination is made at  2740  whether data  2714  of the second blob type (e.g., a relatively large blob, which may include one or more smaller blobs) is a new track. If so, a track is initialized at  2742 , otherwise, the tracked object data stored in track database  2736  and the track may be extended or updated by track updater  2742 . Track classification engine  2762  is coupled to track database  2736  to identify and update/modify tracks by, for example, adding, removing or modifying track-related data. 
       FIG. 28  is a diagram depicting a simulator configured to simulate an autonomous vehicle in a synthetic environment, according to various embodiments. Diagram  2800  includes a simulator  2840  that is configured to generate a simulated environment  2803 . As shown, simulator  2840  is configured to use reference data  2822  (e.g., 3D map data and/or other map or route data including RNDF data or similar road network data) to generate simulated geometries, such as simulated surfaces  2892   a  and  2892   b , within simulated environment  2803 . Simulated surfaces  2892   a  and  2892   b  may simulate walls or front sides of buildings adjacent a roadway. Simulator  2840  may also pre-generated or procedurally generated use dynamic object data  2825  to simulate dynamic agents in a synthetic environment. An example of a dynamic agent is simulated dynamic object  2801 , which is representative of a simulated cyclist having a velocity. The simulated dynamic agents may optionally respond to other static and dynamic agents in the simulated environment, including the simulated autonomous vehicle. For example, simulated object  2801  may slow down for other obstacles in simulated environment  2803  rather than follow a preset trajectory, thereby creating a more realistic simulation of actual dynamic environments that exist in the real world. 
     Simulator  2840  may be configured to generate a simulated autonomous vehicle controller  2847 , which includes synthetic adaptations of a perception engine  2866 , a localizer  2868 , a motion controller  2862 , and a planner  2864 , each of which may have functionalities described herein within simulated environment  2803 . Simulator  2840  may also generate simulated interfaces (“I/F”)  2849  to simulate the data exchanges with different sensors modalities and different sensor data formats. As such, simulated interface  2849  may simulate a software interface for packetized data from, for example, a simulated LIDAR sensor  2872 . Further, simulator  2840  may also be configured to generate a simulated autonomous vehicle  2830  that implements simulated AV controller  2847 . Simulated autonomous vehicle  2830  includes simulated LIDAR sensors  2872 , simulated camera or image sensors  2874 , and simulated radar sensors  2876 . In the example shown, simulated LIDAR sensor  2872  may be configured to generate a simulated laser consistent with ray trace  2892 , which causes generation of simulated sensor return  2891 . Note that simulator  2840  may simulate the addition of noise or other environmental effects on sensor data (e.g., added diffusion or reflections that affect simulated sensor return  2891 , etc.). Further yet, simulator  2840  may be configured to simulate a variety of sensor defects, including sensor failure, sensor miscalibration, intermittent data outages, and the like. 
     Simulator  2840  includes a physics processor  2850  for simulating the mechanical, static, dynamic, and kinematic aspects of an autonomous vehicle for use in simulating behavior of simulated autonomous vehicle  2830 . For example, physics processor  2850  includes a content mechanics module  2851  for simulating contact mechanics, a collision detection module  2852  for simulating the interaction between simulated bodies, and a multibody dynamics module  2854  to simulate the interaction between simulated mechanical interactions. 
     Simulator  2840  also includes a simulator controller  2856  configured to control the simulation to adapt the functionalities of any synthetically-generated element of simulated environment  2803  to determine cause-effect relationship, among other things. Simulator  2840  includes a simulator evaluator  2858  to evaluate the performance synthetically-generated element of simulated environment  2803 . For example, simulator evaluator  2858  may analyze simulated vehicle commands  2880  (e.g., simulated steering angles and simulated velocities) to determine whether such commands are an appropriate response to the simulated activities within simulated environment  2803 . Further, simulator evaluator  2858  may evaluate interactions of a teleoperator  2808  with the simulated autonomous vehicle  2830  via teleoperator computing device  2804 . Simulator evaluator  2858  may evaluate the effects of updated reference data  2827 , including updated map tiles and route data, which may be added to guide the responses of simulated autonomous vehicle  2830 . Simulator evaluator  2858  may also evaluate the responses of simulator AV controller  2847  when policy data  2829  is updated, deleted, or added. The above-description of simulator  2840  is not intended to be limiting. As such, simulator  2840  is configured to perform a variety of different simulations of an autonomous vehicle relative to a simulated environment, which include both static and dynamic features. For example, simulator  2840  may be used to validate changes in software versions to ensure reliability. Simulator  2840  may also be used to determine vehicle dynamics properties and for calibration purposes. Further, simulator  2840  may be used to explore the space of applicable controls and resulting trajectories so as to effect learning by self-simulation. 
       FIG. 29  is an example of a flow chart to simulate various aspects of an autonomous vehicle, according to some embodiments. Flow chart  2900  begins at  2902 , at which reference data including three dimensional map data is received into a simulator. Dynamic object data defining motion patterns for a classified object may be retrieved at  2904 . At  2906 , a simulated environment is formed based on at least three dimensional (“3D”) map data and the dynamic object data. The simulated environment may include one or more simulated surfaces. At  2908 , an autonomous vehicle is simulated that includes a simulated autonomous vehicle controller that forms part of a simulated environment. The autonomous vehicle controller may include a simulated perception engine and a simulated localizer configured to receive sensor data. At  2910 , simulated sensor data are generated based on data for at least one simulated sensor return, and simulated vehicle commands are generated at  2912  to cause motion (e.g., vectored propulsion) by a simulated autonomous vehicle in a synthetic environment. At  2914 , simulated vehicle commands are evaluated to determine whether the simulated autonomous vehicle behaved consistent with expected behaviors (e.g., consistent with a policy). 
       FIG. 30  is an example of a flow chart to generate map data, according to some embodiments. Flow chart  3000  begins at  3002 , at which trajectory data is retrieved. The trajectory data may include trajectories captured over a duration of time (e.g., as logged trajectories). At  3004 , at least localization data may be received. The localization data may be captured over a duration of time (e.g., as logged localization data). At  3006 , a camera or other image sensor may be implemented to generate a subset of the localization data. As such, the retrieved localization data may include image data. At  3008 , subsets of localization data are aligned to identifying a global position (e.g., a global pose). At  3010 , three dimensional (“3D”) map data is generated based on the global position, and at  3012 , the 3 dimensional map data is available for implementation by, for example, a manual route data editor (e.g., including a manual road network data editor, such as an RNDF editor), an automated route data generator (e.g., including an automatic road network generator, including an automatic RNDF generator), a fleet of autonomous vehicles, a simulator, a teleoperator computing device, and any other component of an autonomous vehicle service. 
       FIG. 31  is a diagram depicting an architecture of a mapping engine, according to some embodiments. Diagram  3100  includes a 3D mapping engine that is configured to receive trajectory log data  3140 , LIDAR log data  3172 , camera log data  3174 , radar log data  3176 , and other optional logged sensor data (not shown). Logic  3141  includes a loop-closure detector  3150  configured to detect whether sensor data indicates a nearby point in space has been previously visited, among other things. Logic  3141  also includes a registration controller  3152  for aligning map data, including 3D map data in some cases, relative to one or more registration points. Further, logic  3141  provides data  3142  representing states of loop closures for use by a global pose graph generator  3143 , which is configured to generate pose graph data  3145 . In some examples, pose graph data  3145  may also be generated based on data from registration refinement module  3146 . Logic  3144  includes a 3D mapper  3154  and a LIDAR self-calibration unit  3156 . Further, logic  3144  receives sensor data and pose graph data  3145  to generate 3D map data  3120  (or other map data, such as 4D map data). In some examples, logic  3144  may implement a truncated sign distance function (“TSDF”) to fuse sensor data and/or map data to form optimal three-dimensional maps. Further, logic  3144  is configured to include texture and reflectance properties. 3D map data  3120  may be released for usage by a manual route data editor  3160  (e.g., an editor to manipulate Route data or other types of route or reference data), an automated route data generator  3162  (e.g., logic to configured to generate route data or other types of road network or reference data), a fleet of autonomous vehicles  3164 , a simulator  3166 , a teleoperator computing device  3168 , and any other component of an autonomous vehicle service. Mapping engine  3110  may capture semantic information from manual annotation or automatically-generated annotation as well as other sensors, such as sonar or instrumented environment (e.g., smart stop-lights). 
       FIG. 32  is a diagram depicting an autonomous vehicle application, according to some examples. Diagram  3200  depicts a mobile computing device  3203  including an autonomous service application  3240  that is configured to contact an autonomous vehicle service platform  3201  to arrange transportation of user  3202  via an autonomous vehicle  3230 . As shown, autonomous service application  3240  may include a transportation controller  3242 , which may be a software application residing on a computing device (e.g., a mobile phone  3203 , etc.). Transportation controller  3242  is configured to receive, schedule, select, or perform operations related to autonomous vehicles and/or autonomous vehicle fleets for which a user  3202  may arrange transportation from the user&#39;s location to a destination. For example, user  3202  may open up an application to request vehicle  3230 . The application may display a map and user  3202  may drop a pin to indicate their destination within, for example, a geo-fenced region. Alternatively, the application may display a list of nearby pre-specified pick-up locations, or provide the user with a text entry field in which to type a destination either by address or by name. 
     Further to the example shown, autonomous vehicle application  3240  may also include a user identification controller  3246  that may be configured to detect that user  3202  is in a geographic region, or vicinity, near autonomous vehicle  3230 , as the vehicle approaches. In some situations, user  3202  may not readily perceive or identity autonomous vehicle  3230  as it approaches for use by user  3203  (e.g., due to various other vehicles, including trucks, cars, taxis, and other obstructions that are typical in city environments). In one example, autonomous vehicle  3230  may establish a wireless communication link  3262  (e.g., via a radio frequency (“RF”) signal, such as WiFi or Bluetooth®, including BLE, or the like) for communicating and/or determining a spatial location of user  3202  relative to autonomous vehicle  3230  (e.g., using relative direction of RF signal and signal strength). In some cases, autonomous vehicle  3230  may detect an approximate geographic location of user  3202  using, for example, GPS data or the like. A GPS receiver (not shown) of mobile computing device  3203  may be configured to provide GPS data to autonomous vehicle service application  3240 . Thus, user identification controller  3246  may provide GPS data via link  3260  to autonomous vehicle service platform  3201 , which, in turn, may provide that location to autonomous vehicle  3230  via link  3261 . Subsequently, autonomous vehicle  3230  may determine a relative distance and/or direction of user  3202  by comparing the user&#39;s GPS data to the vehicle&#39;s GPS-derived location. 
     Autonomous vehicle  3230  may also include additional logic to identify the presence of user  3202 , such that logic configured to perform face detection algorithms to detect either user  3202  generally, or to specifically identify the identity (e.g., name, phone number, etc.) of user  3202  based on the user&#39;s unique facial characteristics. Further, autonomous vehicle  3230  may include logic to detect codes for identifying user  3202 . Examples of such codes include specialized visual codes, such as QR codes, color codes, etc., specialized audio codes, such as voice activated or recognized codes, etc., and the like. In some cases, a code may be an encoded security key that may be transmitted digitally via link  3262  to autonomous vehicle  3230  to ensure secure ingress and/or egress. Further, one or more of the above-identified techniques for identifying user  3202  may be used as a secured means to grant ingress and egress privileges to user  3202  so as to prevent others from entering autonomous vehicle  3230  (e.g., to ensure third party persons do not enter an unoccupied autonomous vehicle prior to arriving at user  3202 ). According to various examples, any other means for identifying user  3202  and providing secured ingress and egress may also be implemented in one or more of autonomous vehicle service application  3240 , autonomous vehicle service platform  3201 , and autonomous vehicle  3230 . 
     To assist user  3302  in identifying the arrival of its requested transportation, autonomous vehicle  3230  may be configured to notify or otherwise alert user  3202  to the presence of autonomous vehicle  3230  as it approaches user  3202 . For example, autonomous vehicle  3230  may activate one or more light-emitting devices  3280  (e.g., LEOs) in accordance with specific light patterns. In particular, certain light patterns are created so that user  3202  may readily perceive that autonomous vehicle  3230  is reserved to service the transportation needs of user  3202 . As an example, autonomous vehicle  3230  may generate light patterns  3290  that may be perceived by user  3202  as a “wink,” or other animation of its exterior and interior lights in such a visual and temporal way. The patterns of light  3290  may be generated with or without patterns of sound to identify to user  3202  that this vehicle is the one that they booked. 
     According to some embodiments, autonomous vehicle user controller  3244  may implement a software application that is configured to control various functions of an autonomous vehicle. Further, an application may be configured to redirect or reroute the autonomous vehicle during transit to its initial destination. Further, autonomous vehicle user controller  3244  may be configured to cause on-board logic to modify interior lighting of autonomous vehicle  3230  to effect, for example, mood lighting. Controller  3244  may also control a source of audio (e.g., an external source such as Spotify, or audio stored locally on the mobile computing device  3203 ), select a type of ride (e.g., modify desired acceleration and braking aggressiveness, modify active suspension parameters to select a set of “road-handling” characteristics to implement aggressive driving characteristics, including vibrations, or to select “soft-ride” qualities with vibrations dampened for comfort), and the like. For example, mobile computing device  3203  may be configured to control HVAC functions as well, like ventilation and temperature. 
       FIGS. 33 to 35  illustrate examples of various computing platforms configured to provide various functionalities to components of an autonomous vehicle service, according to various embodiments. In some examples, computing platform  3300  may be used to implement computer programs, applications, methods, processes, algorithms, or other software to perform the above-described techniques. 
     Note that various structures and/or functionalities of  FIG. 33  are applicable to  FIGS. 34 and 35 , and, as such, some elements in those figures may be discussed in the context of  FIG. 33 . 
     In some cases, computing platform  3300  can be disposed in any device, such as a computing device  3390   a , which may be disposed in an autonomous vehicle  3391 , and/or mobile computing device  3390   b.    
     Computing platform  3300  includes a bus  3302  or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor  3304 , system memory  3306  (e.g., RAM, etc.), storage device  3308  (e.g., ROM, etc.), an in-memory cache (which may be implemented in RAM  3306  or other portions of computing platform  3300 ), a communication interface  3313  (e.g., an Ethernet or wireless controller, a Bluetooth controller, NFC logic, etc.) to facilitate communications via a port on communication link  3321  to communicate, for example, with a computing device, including mobile computing and/or communication devices with processors. Processor  3304  can be implemented with one or more graphics processing units (“GPUs”), with one or more central processing units (“CPUs”), such as those manufactured by Intel® Corporation, or one or more virtual processors, as well as any combination of CPUs and virtual processors. Computing platform  3300  exchanges data representing inputs and outputs via input-and-output devices  3301 , including, but not limited to, keyboards, mice, audio inputs (e.g., speech-to-text devices), user interfaces, displays, monitors, cursors, touch-sensitive displays, LCD or LED displays, and other I/O-related devices. 
     According to some examples, computing platform  3300  performs specific operations by processor  3304  executing one or more sequences of one or more instructions stored in system memory  3306 , and computing platform  3300  can be implemented in a client-server arrangement, peer-to-peer arrangement, or as any mobile computing device, including smart phones and the like. Such instructions or data may be read into system memory  3306  from another computer readable medium, such as storage device  3308 . In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation. Instructions may be embedded in software or firmware. The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor  3304  for execution. Such a medium may take many forms, including but not limited to, nonvolatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks and the like. Volatile media includes dynamic memory, such as system memory  3306 . 
     Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus  3302  for transmitting a computer data signal. 
     In some examples, execution of the sequences of instructions may be performed by computing platform  3300 . According to some examples, computing platform  3300  can be coupled by communication link  3321  (e.g., a wired network, such as LAN, PSTN, or any wireless network, including WiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.) to any other processor to perform the sequence of instructions in coordination with (or asynchronous to) one another. Computing platform  3300  may transmit and receive messages, data, and instructions, including program code (e.g., application code) through communication link  3321  and communication interface  3313 . Received program code may be executed by processor  3304  as it is received, and/or stored in memory  3306  or other non-volatile storage for later execution. 
     In the example shown, system memory  3306  can include various modules that include executable instructions to implement functionalities described herein. System memory  3306  may include an operating system (“O/S”)  3332 , as well as an application  3336  and/or logic module(s)  3359 . In the example shown in  FIG. 33 , system memory  3306  includes an autonomous vehicle (“AV”) controller module  3350  and/or its components (e.g., a perception engine module, a localization module, a planner module, and/or a motion controller module), any of which, or one or more portions of which, can be configured to facilitate an autonomous vehicle service by implementing one or more functions described herein. 
     Referring to the example shown in  FIG. 34 , system memory  3306  includes an autonomous vehicle service platform module  3450  and/or its components (e.g., a teleoperator manager, a simulator, etc.), any of which, or one or more portions of which, can be configured to facilitate managing an autonomous vehicle service by implementing one or more functions described herein. 
     Referring to the example shown in  FIG. 35 , system memory  3306  includes an autonomous vehicle (“AV”) module and/or its components for use, for example, in a mobile computing device. One or more portions of module  3550  can be configured to facilitate delivery of an autonomous vehicle service by implementing one or more functions described herein. 
     Referring back to  FIG. 33 , the structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or a combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. As hardware and/or firmware, the above-described techniques may be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs’), or any other type of integrated circuit. According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof. These can be varied and are not limited to the examples or descriptions provided. 
     In some embodiments, module  3350  of  FIG. 33 , module  3450  of  FIG. 34 , and module  3550  of  FIG. 35 , or one or more of their components, or any process or device described herein, can be in communication (e.g., wired or wirelessly) with a mobile device, such as a mobile phone or computing device, or can be disposed therein. 
     In some cases, a mobile device, or any networked computing device (not shown) in communication with one or more modules  3359  (module  3350  of  FIG. 33 , module  3450  of  FIG. 34 , and module  3550  of  FIG. 35 ) or one or more of its components (or any process or device described herein), can provide at least some of the structures and/or functions of any of the features described herein. As depicted in the above-described figures, the structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or any combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated or combined with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, at least some of the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. For example, at least one of the elements depicted in any of the figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. 
     For example, module  3350  of  FIG. 33 , module  3450  of  FIG. 34 , and module  3550  of  FIG. 35 , or one or more of its components, or any process or device described herein, can be implemented in one or more computing devices (i.e., any mobile computing device, such as a wearable device, an audio device (such as headphones or a headset) or mobile phone, whether worn or carried) that include one or more processors configured to execute one or more algorithms in memory. Thus, at least some of the elements in the above-described figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. These can be varied and are not limited to the examples or descriptions provided. 
     As hardware and/or firmware, the above-described structures and techniques can be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), multi-chip modules, or any other type of integrated circuit. 
     For example, module  3350  of  FIG. 33 , module  3450  of  FIG. 34 , and module  3550  of  FIG. 35 , or one or more of its components, or any process or device described herein, can be implemented in one or more computing devices that include one or more circuits. Thus, at least one of the elements in the above-described figures can represent one or more components of hardware. Or, at least one of the elements can represent a portion of logic including a portion of a circuit configured to provide constituent structures and/or functionalities. 
     According to some embodiments, the term “circuit” can refer, for example, to any system including a number of components through which current flows to perform one or more functions, the components including discrete and complex components. Examples of discrete components include transistors, resistors, capacitors, inductors, diodes, and the like, and examples of complex components include memory, processors, analog circuits, digital circuits, and the like, including field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”). Therefore, a circuit can include a system of electronic components and logic components (e.g., logic configured to execute instructions, such that a group of executable instructions of an algorithm, for example, and, thus, is a component of a circuit). According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof (Le., a module can be implemented as a circuit). In some embodiments, algorithms and/or the memory in which the algorithms are stored are “components” of a circuit. Thus, the term “circuit” can also refer, for example, to a system of components, including algorithms. These can be varied and are not limited to the examples or descriptions provided. 
       FIGS. 36A to 36B  illustrate a high-level block diagram depicting an autonomous vehicle system experiencing a sensor-based anomaly while in operation, according to various embodiments. An autonomous vehicle system  3602  may be surrounded by many moving vehicles  3600  (e.g., moving vehicles  3600   a ,  3600   b , and  3600   c ) in a typical driving scenario. As mentioned above, with respect to  FIG. 3A , the autonomous vehicle system  3602  may include many types of sensors or any quantity of sensors to facilitate perception, including image capture sensors, audio capture sensors, LIDAR, RADAR, GPS, and IMU. As illustrated in  FIG. 36A , LIDAR sensors  3604  (e.g., LIDAR sensors  3604 A,  36048 ,  3604 C, and  36040 ) may capture distance, image intensity, and 3D point cloud data based on laser returns  3608 . 
     An example LIDAR sensor  3604  is a VELODYNE VLP-16 Real-Time 3D LIDAR Sensor, manufactured by Velodyne Acoustics, Inc. in Morgan Hill, Calif. A LIDAR Sensor  3604  creates 360 degree 3D images by using 16 laser/detector pairs mounted in a compact housing. The housing rapidly spins to scan the surrounding environment. The lasers fire thousands of times per second, providing a rich 3D point cloud in real time. A laser return  3608  provides data about the reflectivity of an object with 256-bit resolution independent of laser power and distance over a range from 1 meter to 100 meters. For example, laser returns  3608  from LIDAR sensors  3604 A and  3604 B may be used to identify labeled points  3606   b  and  3606   c  on moving vehicles  3600   b  and  3600   c , respectively. Data representing laser returns  3608  may be stored and processed in the autonomous vehicle system  3602 , as described above. A LIDAR sensor  3604  may be synchronized with GPS data based on GPS-supplied time pulses, enabling the ability to determine the exact GPS location at the time of each firing time of each laser. This enables data representing laser returns  3608  to be geo-referenced in real-time. In another embodiment, a LIDAR sensor may be a single-beam LIDAR sensor. 
     As described above, an autonomous vehicle system  3602  uses various sensors, including LIDAR sensors  3604 , in a perception system to aid in localizing the autonomous vehicle system  3602 . For safety reasons and operational efficiency, the autonomous vehicle system  3602  determines its location and the surrounding environment, such as static objects like lane markings  3610  and curbs  3612  as well as dynamic objects like moving vehicles  3600  in real-time and continuously. The autonomous vehicle system  3602  relies on sensor data to update map tile information, such as new lane markings  3610 , as well as current localized information, such as three moving vehicles  3600  also traveling in the same direction of travel  3614  as the autonomous vehicle  3602 . 
     Illustrated in  FIG. 36A , there are two LIDAR sensors  3604  operating, in redundancy, at the front end of the autonomous vehicle system  3602 , in relation to the direction of travel  3614 . In other embodiments, additional or fewer LIDAR sensors  3604  may be used, and in various other arrangements, such as a curved array of LIDAR sensors  3604 , and/or two LIDAR sensors  3604  at each corner of the autonomous vehicle  3602 . However, as illustrated in  FIG. 36A , a LIDAR sensor  3604 A and a LIDAR sensor  36048  are each mounted at the corners of the autonomous vehicle system  3602 . The LIDAR sensors  3604  have been calibrated such that the laser returns  3608  identify labeled points  3606  accurately and independently, in real-time with synchronized GPS data. 
       FIG. 36B  illustrates a scenario in which one of the LIDAR sensors  3604  experiences an anomaly or other malfunction. A sensor anomaly may be detected when sensor data may be still gathered from the sensor, but the data may be outside of normal operating parameters. Here, failed sensor  3604   b  has reported an indication that the data from the sensor cannot be trusted for various reasons, such as the data being reported is severely miscalibrated, the data being reported does not match expected data (such as a lane marking  3610  that is not in the expected field of perception), as well as complete sensor failure that reports no data or a sensor malfunction. As a result of failed sensor  3604   b , LIDAR sensor  3604 A is relied upon by the autonomous vehicle system  3602  for generating a field of perception until the failed sensor  3604   b  becomes operational again. In one embodiment, the LIDAR sensor  3604 A generates laser returns  3608  that form the field of perception for the autonomous vehicle system  3602  after the autonomous vehicle system  3602  receives an indication of the failed sensor  3604   b.    
     In one embodiment, the autonomous vehicle system  3602  (“AV system”) may include a sensor failure strategies store  3718 , as illustrated in  FIG. 37 .  FIG. 37  illustrates a high-level block diagram of a sensor-based object detection optimization for autonomous vehicles, according to various embodiments. Upon receiving an indication of an anomaly and/or failure of a sensor  3714 , which may include LIDAR sensors  3604 , RADAR sensors  3708 , 1M Us  3712 , and cameras  3710 , a perception system  3702  may utilize a sensor failure module  3704  to process the sensor anomaly and/or failure. A localizer  3716  may include a sensor compensation module  3706 , in one embodiment, to process the incoming sensor data received from sensors  3714  and to identify how the field of perception for the AV system  3602  is affected by the failed sensor. The AV system  3602  may rely on the sensors  3714  and “fuse” the data generated by the heterogeneous types of sensors, such as data from LIDAR sensors  3604  and motion data from IMUs  3712 , in one embodiment. The localizer  3716  may generate a probabilistic map of the current environment, assigning probability scores to labeled objects in the field of perception. These probability scores may be reduced as a result of the failed sensor. A sensor compensation module  3706  may be able to rely on other sensor data to compensate for the lost data stream of the failed sensor. For example, IMU motion data may indicate that the AV system  3602  is traveling in a straight line at a certain velocity such that the expected field of perception may be calculated by the sensor compensation module  3706  using the remaining operational LIDAR sensor  3604 . 
     An AV system  3602  may also include a planner  3722  that includes a trajectory selection module  3724 . The planner  3722  may retrieve one or more strategies from the sensor failure strategies store  3718  to determine one or more trajectories for the AV system  3602 . For example, if the AV system  3602  is currently executing a ride request from a passenger user, the planner  3722  may determine whether the perception system  3702  is operating within safe parameters such that the ride may be completed. In one embodiment, the planner  3722  may decide to execute a maneuver to change the directionality of the AV system  3602  such that the failed sensor is located at the dorsal end, or the rear, of the AV system  3602  in relation to the direction of travel. An example of such a maneuver is described above in relation to  FIG. 3E , where the failed sensor  309  is initially located at the front of the AV system, but then is located at the rear after performing the maneuver. 
     As described above with respect to  FIG. 4  and now with respect to  FIG. 37 , a controller  3726  may be used by an AV system  3602  to control the motion of the vehicle. A log file store  3720  may be used to store sensor data generated by the sensors  3714  as well as trajectories executed by the planner  3722  based on the received sensor data. In one embodiment, the planner  3722  may communicate with a teleoperator system  3750  to ask for additional guidance based on the indication of the failed sensor. One or more trajectories may have been preselected and presented to the teleoperator system  3750  based on a sensor failure strategy retrieved from the sensor failure strategies store  3718 . In another embodiment, the planner  3722  may rely on log file data retrieved from the log file store  3720  to determine and/or select a trajectory for the AV system  3602  to travel. These determinations and calculations may be performed online and in real-time as the vehicle is moving and in operation, in one embodiment. In another embodiment, a sensor failure strategy may include an instruction to arrive at a safe stop based on a failed sensor. After the safe stop, the AV system  3602  may execute one or more courses of action based on the sensor failure strategy retrieved from the store  3718 . 
     In a further embodiment, the AV system  3602  may use one or more sensor failure strategies retrieved from the sensor failure strategies store  3718  and have the planner  3722  decide a course of action from a selected strategy based on log file data retrieved from the log file store  3720  and/or sensor data being generated from sensors  3714 . The sensor failure module  3704  may report data generated from the sensors  3714 , including the failed sensor data and/or anomalous data to the localizer  3716 . The localizer  3716  may assign a probability score of the likelihood of an object being correctly labeled within the field of perception, such as labeled point  3606   b  being correctly correlated with moving vehicle  3600   b . Probability scores such as this may be generated from a sensor compensation module  3706  based on past sensor failures, in one embodiment. In another embodiment, a localizer  3716 , as described above with respect to  FIG. 4 , may generate probabilistic map tiles that assign probabilities to each map tile, which may be 16 cm×16 cm, in one embodiment. As a result, the laser returns  3608  of LIDAR sensor  3604 A, as illustrated in  FIG. 368 , may be solely relied upon for the localizer  3716  generating a map tile associated with the labeled points  3606 , for example. In another embodiment, IMU data indicating the velocity of the AV system  3602  along with GPS data and log file data may be also be used to generate the probability score for the labeled points  3606 . The AV system  3602  is able to compute these probabilities in real-time, as the vehicle is in operation, because of processing power of on-board computers, super-fast processors, and low-latency log file retrieval and storage. 
     Referring to  FIGS. 3B to 3D , each sensor  310  may generate a sensor field  301 . Sensor fields  301  may overlap, such that combined sensor fields  302  and  303 , as illustrated in  FIG. 3C , may two or more sensors  310  contributing to the object classification and/or object detection algorithms. However, these sensor fields may change based on a sensor failure or other sensor anomaly, as illustrated in  FIG. 3D  where sensor  309  has failed. As shown, a blind spot  304  in the field of perception may occur based on a failed sensor  309 . The field of perception, as illustrated in  FIG. 3D , includes sensor fields  301 ,  302 ,  303 ,  305 , and  306 . Other types of sensors may be used to compensate for a blind spot  304  in the field of perception while the AV system is in operation. 
     In an embodiment, a sensor anomaly may be detected upon the AV system determining that sensor data gathered from the sensor includes a range of laser return intensities that are a result of the reflectivity properties various phenomena, such as too much sunlight, directly or indirectly bouncing off glass, water, or other shiny surfaces, and other lights, such as headlights or external lights in the infrared range interfering with the laser returns. Various conditions, such as weather condition, refraction due to differences in air density due to heat waves off a hot road surface, and glass surfaces with water (e.g., windshields, foggy and/or wet windshields) may cause interferences with LIDAR sensors, reducing the range at which light may be received. Other conditions, such as bright direct sunlight and/or high intensity headlights, may impede laser returns because the sensor&#39;s receiver rejects light outside the laser&#39;s operating range. 
     Returning to  FIG. 37 , a sensor recovery strategies store  3718  may be stored locally on the A V system  3602  and updated with new sensor recovery strategies as they become available. Such sensor recovery strategies may be employed based on real-life scenarios, in one embodiment. For example, log files included in the log file store  3720  may be analyzed to produce one or more sensor recovery strategies to be stored in the sensor recovery strategies store  3718 . Other sensor recovery strategies may be simulated offline using a simulator that replicates real driving conditions in a simulated world. 
     Additional sensor recovery strategies may include accessing a map database to determine an alternative trajectory, route, and/or path that avoids one or more conditions that may be causing the anomalous sensor measurements. For example, where the sensor measurements of laser returns may be attributed to too much direct sunlight, alternative paths and/or trajectories may be determined as a sensor recovery strategy included in the store  3718 . The planner  3722  may then select a new trajectory through the trajectory selection module  3724  that includes less direct sunlight based on building blocking the direct sunlight. As a result, the detection of the cause of the sensor anomaly may influence the selection of a sensor recovery strategy, thus modifying the operation of the AV system responsive to the sensor anomaly. 
     In one embodiment, the localizer  3716  may determine a quantifiable measure of how the perception system  3702  is affected by the sensor anomaly and/or failure. A course of action in a sensor recovery strategy store  3718  may include operating the AV system  3602  in a sub-optimal mode of operation that remains within safe levels of operation. In this sub-optimal mode of operation, other sensors may be used and relied upon to ensure a safe level of operation, such as using camera data in conjunction with IMU motion data to compensate for blind spots caused by a failed sensor. 
     The sensor compensation module  3706  may include various methods and techniques that may be used to compensate for different types of sensor anomalies and/or failures. Thus, the type of sensor anomaly or type of sensor failure, as determined by the sensor failure module  3704 , may directly affect how the localizer  3716  uses other sensor data to compensate for the loss and/or degradation in the field of perception. For example, processing camera data may be prioritized based on a failed LIDAR sensor at the front of the AV system  3602 . Similarly, generating probabilistic map tiles based on inferred labeled data points using IMU motion data with a remaining operational LIDAR sensor in conjunction with GPS data may be highly prioritized by a processor or set of processors in the AV system  3602 , as another example. The sensor compensation module  3706  may identify a degradation of a sensor based on any number of conditions, such as bad weather, low battery, low memory utilization, and low storage capacity, and may provide coverage for the lower functioning sensor based on other sensors. 
       FIG. 38  is a network diagram of a system for sensor-based object detection optimization for autonomous vehicles, showing a block diagram of an autonomous vehicle management system, according to an embodiment. The system environment includes one or more AV systems  3602 , teleoperator systems  3750 , user devices  3806 , an autonomous vehicle (“AV”) management system  3800 , and a network  3804 . In alternative configurations, different and/or additional modules can be included in the system. 
     The user devices  3806  may include one or more computing devices that can receive user input and can transmit and receive data via the network  3804 . In one embodiment, the user device  3806  is a conventional computer system executing, for example, a Microsoft Windows-compatible operating system (OS), Apple OS X, and/or a Linux distribution. In another embodiment, the user device  3806  can be a device having computer functionality, such as a personal digital assistant (PDA), mobile telephone, smart-phone, wearable device, etc. The user device  3806  is configured to communicate via network  3804 . The user device  3806  can execute an application, for example, a browser application that allows a user of the user device  3806  to interact with the AV management system  3800 . In another embodiment, the user device  3806  interacts with the AV management system  3800  through an application programming interface (API) that runs on the native operating system of the user device  3806 , such as iOS and ANDROID. 
     In one embodiment, the network  3804  uses standard communications technologies and/or protocols. Thus, the network  3804  can include links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (Wi MAX), 3G, 4G, CDMA, digital subscriber line (DSL), etc. Similarly, the networking protocols used on the network  3804  can include multiprotocol label switching (MPLS), the transmission control protocol/Internet protocol (TCP/IP), the User Datagram Protocol (UDP), the hypertext transport protocol (HTTP), the simple mail transfer protocol (SMTP), and the file transfer protocol (FTP). The data exchanged over the network  204  can be represented using technologies and/or formats including the hypertext markup language (HTML) and the extensible markup language (XML). (in addition, all or some of links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), and Internet Protocol security (IPsec). 
       FIG. 38  contains a block diagram of the AV management system  3800 . The AV management system  3800  includes a sensor recovery scenario store  3802 , a web server  3810 , an API management module  3808 , a sensor recovery module  3812 , and a teleoperator interface module  3814 . In other embodiments, the AV management system  3800  may include additional, fewer, or different modules for various applications. Conventional components such as network interfaces, security functions, load balancers, failover servers, management and network operations consoles, and the like are not shown so as to not obscure the details of the system. 
     The web server  3810  links the AV management system  3800  via the network  3804  to one or more user devices  3806 ; the web server  3810  serves web pages, as well as other web-related content, such as Java, Flash, XML, and so forth. The web server  3810  may provide the functionality of receiving and routing messages between the AV management system  3800  and the user devices  3806 , for example, instant messages, queued messages (e.g., email), text and SMS (short message service) messages, or messages sent using any other suitable messaging technique. The user can send a request to the web server  3810  to provide information, for example, images or videos that are stored in the AV management system  3800  for viewing by the user device(s)  3806 . Additionally, the web server  3810  may provide API functionality to send data directly to native user device operating systems, such as iOS, ANDROID, webOS, and RIM. 
     A sensor recovery scenario store  3802  may store sensor recovery scenarios uploaded by AV systems  3602  connected to the AV management system  3800 . Sensor recovery scenarios may include sensor data captured during a sensor failure or abnormal sensor operation, as well as courses of action taken by the AV systems  3602  in response to the sensor failures. Additionally, sensor recovery scenarios may be generated and stored by other systems connected to or part of the AV management system  3800 , such as simulated scenarios in which suggested strategies have been executed, in a simulated environment, by various simulated AV systems (not pictured). Based on these scenarios, a sensor recovery module  3812  may be configured to provide recommended courses of action and/or strategies to respond to various types of sensor failures. These recommendations may be provided to a teleoperator system  3750  requesting historical data and/or past strategies for handling sensor failures, for example. In another embodiment, such recommendations may be provided to A V systems  3602  based on an indication of a sensor failure. 
     An API management module  3808  may manage one or more adapters needed for the AV management system  3800  to communicate with various systems, such as teleoperator systems  3750  and user devices  3806 . Application programming interfaces (APIs), or adapters, may be used to push data to external tools, websites, and user devices  3806 . Adapters may also be used to receive data from the external systems. In one embodiment, the API management module  3808  manages the amount of connections to the external systems needed to operate efficiently. 
     A sensor recovery module  3812  may analyze and provide information to AV systems  3602  based on received log data and/or indications from the AV systems  3602  that one or more sensors have malfunctioned, in one embodiment. The sensor recovery module  3812  may process the data gathered from multiple AV systems  3602  offline and may rely on various probabilistic techniques, Bayesian inference, and machine learning algorithms to identify optimal strategies in responding to sensor failures over time. 
     A teleoperator interface module  3814  may provide an interface for teleoperator systems  3750  to interact with and provide guidance to AV systems  3602 . In conjunction with the sensor recovery module  3812 , the teleoperator interface module  3814  may provide one or more selected strategies to a teleoperator system  3750  that has been requested to provide assistance to an AV system  3602  experiencing a sensor anomaly and/or failure. For example, an AV system  3602  may detect that a sensor is not operating within normal parameters based on data generated from the sensor not corroborating with other sensor data, such as a lane marking appearing in an unexpected location based on map tile information. In presenting the sensor information from the AV system  3602  to the teleoperator system  3750 , the teleoperator interface module  3814  may retrieve log data and/or other information related to similar sensor anomalies from the sensor failure scenario store  3802 . As a result, the teleoperator system  3750  may confirm, through the teleoperator interface provided by the teleoperator interface module  3814 , that the sensor has failed. A suggested course of action may be selected through the teleoperator interface, in one embodiment. In another embodiment, the AV system  3602  may automatically identify a course of action based on the confirmed sensor failure. 
       FIG. 39  is a high-level flow diagram illustrating a process for sensor-based object detection optimization for autonomous vehicles, according to some examples. An indication of a sensor anomaly may be received  3900  at an autonomous vehicle system. The indication of a sensor anomaly may be received  3900  based on a determination that sensor data measurements gathered from a particular sensor may not be correct based on expected measurements. In another embodiment, sensor data for a particular sensor may not be received, indicating that the sensor has failed or is otherwise malfunctioning. At least one sensor recovery strategy may be determined  3902  based on the sensor anomaly. Sensor recovery strategies may be pre-generated based on the arrangement of sensors on the autonomous vehicle system and stored locally on the autonomous vehicle system. In one embodiment, a sensor recovery strategy may be determined  3902  by retrieving and/or receiving strategies from another autonomous vehicle system and/or an autonomous vehicle management system  3800 . 
     Optionally, a guidance request may be sent  3904  to a teleoperator system and/or an AV management system based on the at least one sensor recovery strategy and the sensor anomaly. This guidance request may be a request for more information, such as historical log data of similar sensor anomalies and/or failures, in one embodiment. In another embodiment, the guidance request may include a request for a suggestion on a course of action. The suggestion may be received  3906 , optionally, based on the guidance request in association with the sensor anomaly. The suggestion may be made through a selection on teleoperator interface, for example, provided on a teleoperator system. In one embodiment, a suggestion may be made based on offline analysis by an AV management system of past sensor anomalies and/or failures. A course of action included in the at least one sensor recovery strategy may be executed  3908 . Optionally, the course of action may be executed  3908  in accordance with the received suggestion. 
       FIGS. 40 and 41  illustrate exemplary computing platforms disposed in devices configured to optimize sensor-based object detection in accordance with various embodiments. In some examples, computing platforms  4000  and  4100  may be used to implement computer programs, applications, methods, processes, algorithms, or other software to perform the above-described techniques. 
     In some cases, computing platform can be disposed in wearable device  10  or implement, a mobile computing device  4090   b  or  4190   b , or any other device, such as a computing device  4090   a  or  4190   a.    
     Computing platform  4000  or  4100  includes a bus  4004  or  4104  or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor  4006  or  4106 , system memory  4010  or  4110  (e.g., RAM, etc.), storage device  4008  or  4108  (e.g., ROM, etc.), a communication interface  4012  or  4112  (e.g., an Ethernet or wireless controller, a Bluetooth controller, etc.) to facilitate communications via a port on communication link  4014  or  4114  to communicate, for example, with a computing device, including mobile computing and/or communication devices with processors. Processor  4006  or  4106  can be implemented with one or more central processing units (“CPUs”), such as those manufactured by Intel® Corporation, or one or more virtual processors, as well as any combination of CPUs and virtual processors. Computing platform  4000  or  4100  exchanges data representing inputs and outputs via input-and-output devices  4002  or  4102 , including, but not limited to, keyboards, mice, audio inputs (e.g., speech-to-text devices), user interfaces, displays, monitors, cursors, touch-sensitive displays, LCD or LED displays, and other I/O-related devices. 
     According to some examples, computing platform  4000  or  4100  performs specific operations by processor  4006  or  4106  executing one or more sequences of one or more instructions stored in system memory  4010  or  4110 , and computing platform  4000  or  4100  can be implemented in a client-server arrangement, peer-to-peer arrangement, or as any mobile computing device, including smart phones and the like. Such instructions or data may be read into system memory  4010  or  4110  from another computer readable medium, such as storage device  4008  or  4108 . In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation. Instructions may be embedded in software or firmware. The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor  4006  or  4106  for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks and the like. Volatile media includes dynamic memory, such as system memory  4010  or  4110 . 
     Common forms of ‘computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus  4004  or  4104  for transmitting a computer data signal. 
     In some examples, execution of the sequences of instructions may be performed by computing platform  4000  or  4100 . According to some examples, computing platform  4000  or  4100  can be coupled by communication link  4014  or  4114  (e.g., a wired network, such as LAN, PSTN, or any wireless network, including WiFi of various standards and protocols, Blue Tooth®, Zig-Bee, etc.) to any other processor to perform the sequence of instructions in coordination with (or asynchronous to) one another. Computing platform  4000  or  4100  may transmit and receive messages, data, and instructions, including program code (e.g., application code) through communication link  4014  or  4114  and communication interface  4012  or  4112 . Received program code may be executed by processor  4006  or  4106  as it is received, and/or stored in memory  4010  or  4110  or other non-volatile storage for later execution. 
     In the example shown, system memory  4010  or  4110  can include various modules that include executable instructions to implement functionalities described herein. System memory  4010  or  4110  may include an operating system (“O/S”)  4030  or  4130 , as well as an application  4032  or  4132  and/or logic module  4050  or  4150 . In the example shown in  FIG. 40 , system memory  4010  includes a sensor recovery module  3812 , an API management module  3808 , and a teleoperator interface module  3814 . The system memory  4150  shown in  FIG. 41  includes a perception system  3702  that includes a sensor failure module  3704 , a localizer module  4134  that includes a sensor compensation module  3706 , and a planner module  4136  that includes a trajectory selection module  3724 . One or more of the modules included in memory  4010  or  4110  can be configured to provide or consume outputs to implement one or more functions described herein. 
     In at least some examples, the structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or a combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. As hardware and/or firmware, the above-described techniques may be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), or any other type of integrated circuit. According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof. These can be varied and are not limited to the examples or descriptions provided. 
     In some embodiments, an AV management system or one or more of its components, or any process or device described herein, can be in communication (e.g., wired or wirelessly) with a mobile device, such as a mobile phone or computing device, or can be disposed therein. 
     In some cases, a mobile device, or any networked computing device (not shown) in communication with an action alert controller or one or more of its components (or any process or device described herein), can provide at least some of the structures and/or functions of any of the features described herein. As depicted in the above-described figures, the structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or any combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated or combined with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, at least some of the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. For example, at least one of the elements depicted in any of the figure can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. 
     For example, a perception system  3702  or any of its one or more components, or any process or device described herein, can be implemented in one or more computing devices (i.e., any mobile computing device, such as a wearable device, an audio device (such as headphones or a headset) or mobile phone, whether worn or carried) that include one or more processors configured to execute one or more algorithms in memory. Thus, at least some of the elements in the above-described figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. These can be varied and are not limited to the examples or descriptions provided. 
     As hardware and/or firmware, the above-described structures and techniques can be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), multi-chip modules, or any other type of integrated circuit. 
     For example, an autonomous vehicle management system, including one or more components, or any process or device described herein, can be implemented in one or more computing devices that include one or more circuits. Thus, at least one of the elements in the above-described figures can represent one or more components of hardware. Or, at least one of the elements can represent a portion of logic including a portion of circuit configured to provide constituent structures and/or functionalities. 
     According to some embodiments, the term “circuit” can refer, for example, to any system including a number of components through which current flows to perform one or more functions, the components including discrete and complex components. Examples of discrete components include transistors, resistors, capacitors, inductors, diodes, and the like, and examples of complex components include memory, processors, analog circuits, digital circuits, and the like, including field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”). Therefore, a circuit can include a system of electronic components and logic components (e.g., logic configured to execute instructions, such that a group of executable instructions of an algorithm, for example, and, thus, is a component of a circuit). According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof (i.e., a module can be implemented as a circuit). In some embodiments, algorithms and/or the memory in which the algorithms are stored are “components” of a circuit. Thus, the term “circuit” can also refer, for example, to a system of components, including algorithms. These can be varied and are not limited to the examples or descriptions provided. 
     Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described invention techniques. The disclosed examples are illustrative and not restrictive. 
     The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure. 
     Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof. 
     Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. 
     Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. 
     Embodiments of the invention may also relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a non-transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein. 
     Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.