Patent Publication Number: US-2023147874-A1

Title: Obstacle detection based on other vehicle behavior

Description:
BACKGROUND 
     1. Technical Field 
     The disclosed technology provides solutions for improving autonomous vehicle (AV) routing and in particular, for improving obstacle detection based on observations of other vehicles. 
     2. Introduction 
     Autonomous vehicles (AVs) are vehicles having computers and control systems that perform driving and navigation tasks that are conventionally performed by a human driver. As AV technologies continue to advance, they will be increasingly used to improve transportation efficiency and safety. As such, AVs will need to perform many of the functions that are conventionally performed by human drivers, such as performing navigation and routing tasks necessary to provide a safe and efficient transportation. Such tasks may require the collection and processing of large quantities of data using various sensor types, including but not limited to cameras and/or Light Detection and Ranging (LiDAR) sensors disposed on the AV. In some instances, the collected data can be used by the AV to perform tasks relating to routing, planning and obstacle avoidance. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain features of the subject technology are set forth in the appended claims. However, the accompanying drawings, which are included to provide further understanding, illustrate disclosed aspects and together with the description serve to explain the principles of the subject technology. In the drawings: 
         FIG.  1    conceptually illustrates an example environment in which an obstacle detection and avoidance process of the disclosed technology can be implemented. 
         FIG.  2    illustrates an example of how risk-profile scoring can be performed using an area grid applied to the environment of  FIG.  1   , according to some aspects of the disclosed technology. 
         FIG.  3    illustrates a flow diagram of an example process for implementing an obstacle avoidance procedure based on observed behaviors of one or more other vehicles, according to some aspects of the disclosed technology. 
         FIG.  4    illustrates an example system environment that can be used to facilitate AV dispatch and operations, according to some aspects of the disclosed technology. 
         FIG.  5    illustrates an example processor-based system with which some aspects of the subject technology can be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description set forth below is intended as a description of various configurations of the subject technology and is not intended to represent the only configurations in which the subject technology can be practiced. The appended drawings are incorporated herein and constitute a part of the detailed description. The detailed description includes specific details for the purpose of providing a more thorough understanding of the subject technology. However, it will be clear and apparent that the subject technology is not limited to the specific details set forth herein and may be practiced without these details. In some instances, structures and components are shown in block diagram form in order to avoid obscuring the concepts of the subject technology. 
     As described herein, one aspect of the present technology is the gathering and use of data available from various sources to improve quality and experience. The present disclosure contemplates that in some instances, this gathered data may include personal information. The present disclosure contemplates that the entities involved with such personal information respect and value privacy policies and practices. 
     Perception systems of autonomous vehicles are designed to detect various objects in the surrounding environment in order to execute effective navigation and planning operations. To ensure passenger safety, such systems are designed to detect and avoid potential road areas that contain objects/features that may cause safety concerns, such as blockages or obstacles resulting from road debris, potholes, puddles, and/or stationary vehicles (such as parked cars), etc. One limitation of conventional perception systems is that they are highly reliant on environmental data, such as camera and/or LiDAR data, in which obstacle visibility is a prerequisite for detection. That is, conventional perception systems greatly rely on sensor data collected from direct observation, such as image or Light Detection and Ranging (LiDAR) data that is acquired from vehicle sensors. As such, obstacles that are occluded, for example, by other objects (e.g., vehicles), may not be recognized conventional perception systems. 
     Aspects of the disclosed technology provide solutions for improving obstacle detection functions performed by AV perception systems, and therefore the ability for AVs to better reason about the environment in which they operate. In some aspects, a perception system of the disclosed technology can identify/detect behavior about various traffic participants (e.g., other vehicles), in order to make inferences about the existence and location of potentially dangerous road areas, such as those that include obstacles not directly observable by the ego vehicle&#39;s sensors. 
     In some aspects, an AV of the disclosed technology can use collected environmental data (e.g., camera, LiDAR, and/or radar data) to make inferences about the existence and location of potentially dangerous objects or conditions, based on the behavior of surrounding vehicles. For example, avoidance behaviors observed in other vehicles can be used to make inferences about the existence and location of potential obstacles, and used to inform routing decisions needed to avoid the corresponding road areas (e.g., lane and/or road segments), believed to contain the obstacle. Obstacles can include any of a variety of objects and/or scenarios that a navigating vehicle may wish to avoid; by way of example, obstacles may include, but are not limited to: standing water, ice, potholes, road debris etc. Obstacles may also include interactions between traffic and/or non-traffic participants that may be best avoided by the navigating ego vehicle, such as accidents involving other vehicles and/or pedestrians. 
     In some implementations, various road areas can be associated with specific areas (e.g., grid areas or cells) by the ego AV. In this manner, each grid cell can be associated with a risk-profile score that is based at least in part on the observed avoidance behaviors. By taking consideration of the risk-profile score/s associated with each grid-cell, the AV perception system can better reason about potentially dangerous obstacles not directly detectable by the AV&#39;s sensor systems, and can make routing and navigation decisions accordingly. 
       FIG.  1    conceptually illustrates an example environment  100  in which an obstacle avoidance process of the disclosed technology can be implemented. In the example of  FIG.  1   , an autonomous vehicle (AV)  102  is positioned behind another vehicle  108  such that pothole  106 , on roadway  101 , is not observable by the sensor systems of the (ego) AV  102 . However, in based on avoidance behaviors observed with respect to vehicle  108  (which is directly observable by sensor systems of AV  102 ), the AV  102  can determine that a road area on the roadway  101 , corresponding with pothole  106 , should be avoided and can plot a trajectory accordingly. 
     In some aspects, areas that are inferred to contain some feature or characteristic that should be avoided can be scored (e.g., given a risk-profile score). The risk-profile score can be based on the behavior of one or more vehicles with respect to that particular area. For example, if multiple vehicles are observed avoiding a particular area, the risk score may be greater/higher than if only one vehicle is observed avoiding the area. Scoring of such observations may also be based on other factors, such as the number of other vehicles that can be observed. By way of example, a fewer number of vehicles observed avoiding a section of road on a relatively empty or remote highway may result in a higher risk-profile score being associated with that road section, as compared to the risk-profile that may be calculated if avoidance behaviors were observed in a greater number of vehicles on a more populated roadway. 
     Risk-profile scores can also be based on conditions that are directly observable or measurable by one or more AV sensors (e.g., LiDAR, camera, and/or radar sensors, etc.). By way of example, if visibility is reduced (e.g., due to weather conditions), the observed avoidance behaviors of vehicles may result in a greater risk-profile score for a particular area (cell), as compared to that which would be assessed under normal visibility conditions. Depending on the desired implementation, signals regarding roadway conditions (e.g., weather data), may be directly measured by one or more sensors of the ego vehicle (e.g., AV sensors), or may be received by the AV from one or more remote system/s, such as a third-party system that is configured to provide weather and/or traffic updates. In some approaches, risk-profile scores can be computed or determined for discrete areas of the roadway, such as discrete portions of a road segment and/or lane, etc. Further details regarding the use of area grids for assigning discrete map areas (grid cells) to a risk-profile score are discussed in further detail with respect to  FIG.  2   , below. 
       FIG.  2    illustrates an example of how a risk-profile scoring can be performed using an area grid  200  applied to the environment of  FIG.  1   , according to some aspects of the disclosed technology. In the example of  FIG.  2   , area grid  200  includes a number of grid areas (cells) that define various portions of the roadway  101  surrounding AV  102 . Further to the example of  FIG.  1   , cells  202 ,  204 ,  206  and  208  define portions of area grid  200  that correspond with the location of pothole  106 . In this scenario, although pothole  106  is not directly observable by AV  102 , the behavior of vehicle  108  can be directly observed and used by AV  102  to reason about risks associated with unobservable areas of roadway  101 , e.g., cells  202 - 208 . By way of example, based on the behavior of vehicle  108 , AV  102  can determine/compute a risk-profile score for one or more cells for area grid  200 . 
     In some approaches, the risk-profile score may include a quantitative score that is used to compare one or more cells, e.g., for the purpose of route planning and navigation. By way of example, the planning/routing functions of AV  102  can be configured to select routing/navigation paths such that the AV traverses only road areas associated with lower risk-profile scores, relative to other roadway segments. In other scenarios, risk-profile may be more qualitative, and certain cells may be identified for avoidance. 
       FIG.  3    illustrates a flow diagram of an example process  300  for implementing an obstacle avoidance procedure based on observed behaviors of one or more other (non-ego) vehicles. At step  302 , the process  300  includes collecting environmental data, via one or more environmental sensors, about an environment around an autonomous vehicle, i.e. the ego AV, such as AV  102 , discussed above with respect to  FIGS.  1  and  2   . Depending on the AV implementation, the environmental data can include sensor data from one or more of a variety of sensors, including but not limited to: LiDAR/s, radar/s, cameras, thermal cameras, accelerometers, gyroscopic sensors, and/or inertial measurement units (IMUs), etc. As discussed above, the environmental data can include data pertaining to a roadway navigated by the autonomous vehicle and one or more other vehicles navigating the roadway. 
     At step  304 , the process  304  includes processing the environmental data to generate an area grid comprising one or more grid sections. In some implementations, the area grid can be generated by processing the collected environmental data to generate a top-down (bird&#39;s eye) view of the roadway (e.g., roadway  101 ) and surrounding environs, for example, where each grid section corresponds with a different area of the roadway. In some aspects, AV location information and/or localization information for various objects observed by the AV may be used to ensure that each grid-cell is approximately the same dimensions, such that each cell corresponds with approximately the same amount of roadway area. 
     At step  306 , the process  300  includes determining a risk-profile for the one or more grid sections based on the environmental data. As discussed above, risk-profile assessments (scores) can be based on a combination of factors, including but not limited to detected conditions (e.g., visibility) or objects that can be directly measured using sensor data. Object detection can be performed to associated observable objects with particular grid-cells. By way of example, a machine-learning based object detection system may be deployed to identify semantic labels for various objects in an environment, and to associate those semantic labels with a given cell. In turn, risk-profile scoring can be based on inferences that are made using the environmental (sensor) data, as well as contextual data that is provided by surrounding grid-cells (and their corresponding semantic labels), and behaviors observed by other vehicles. 
     By way of example, risk-profile scores for one or more grid cells can be used by the AV to resolve a route needed to avoid various grid sections that are inferred to contain potentially dangerous features, such as obstacles that may pose dangers or difficulties to AV operations. 
     Turning now to  FIG.  4    illustrates an example of an AV management system  400 . One of ordinary skill in the art will understand that, for the AV management system  400  and any system discussed in the present disclosure, there can be additional or fewer components in similar or alternative configurations. The illustrations and examples provided in the present disclosure are for conciseness and clarity. Other embodiments may include different numbers and/or types of elements, but one of ordinary skill the art will appreciate that such variations do not depart from the scope of the present disclosure. 
     AV management system  400  includes an AV  402 , a data center  450 , and a client computing device  470 . The AV  402 , the data center  450 , and the client computing device  470  can communicate with one another over one or more networks (not shown), such as a public network (e.g., the Internet, an Infrastructure as a Service (IaaS) network, a Platform as a Service (PaaS) network, a Software as a Service (SaaS) network, other Cloud Service Provider (CSP) network, etc.), a private network (e.g., a Local Area Network (LAN), a private cloud, a Virtual Private Network (VPN), etc.), and/or a hybrid network (e.g., a multi-cloud or hybrid cloud network, etc.). 
     AV  402  can navigate about roadways without a human driver based on sensor signals generated by multiple sensor systems  404 ,  406 , and  408 . The sensor systems  404 - 408  can include different types of sensors and can be arranged about the AV  402 . For instance, the sensor systems  404 - 408  can comprise Inertial Measurement Units (IMUs), cameras (e.g., still image cameras, video cameras, etc.), light sensors (e.g., LIDAR systems, ambient light sensors, infrared sensors, etc.), RADAR systems, GPS receivers, audio sensors (e.g., microphones, Sound Navigation and Ranging (SONAR) systems, ultrasonic sensors, etc.), engine sensors, speedometers, tachometers, odometers, altimeters, tilt sensors, impact sensors, airbag sensors, seat occupancy sensors, open/closed door sensors, tire pressure sensors, rain sensors, and so forth. For example, the sensor system  404  can be a camera system, the sensor system  406  can be a LIDAR system, and the sensor system  408  can be a RADAR system. Other embodiments may include any other number and type of sensors. 
     AV  402  can also include several mechanical systems that can be used to maneuver or operate AV  402 . For instance, the mechanical systems can include vehicle propulsion system  430 , braking system  432 , steering system  434 , safety system  436 , and cabin system  438 , among other systems. Vehicle propulsion system  430  can include an electric motor, an internal combustion engine, or both. The braking system  432  can include an engine brake, brake pads, actuators, and/or any other suitable componentry configured to assist in decelerating AV  402 . The steering system  434  can include suitable componentry configured to control the direction of movement of the AV  402  during navigation. Safety system  436  can include lights and signal indicators, a parking brake, airbags, and so forth. The cabin system  438  can include cabin temperature control systems, in-cabin entertainment systems, and so forth. In some embodiments, the AV  402  may not include human driver actuators (e.g., steering wheel, handbrake, foot brake pedal, foot accelerator pedal, turn signal lever, window wipers, etc.) for controlling the AV  402 . Instead, the cabin system  438  can include one or more client interfaces, e.g., Graphical User Interfaces (GUIs), Voice User Interfaces (VUIs), etc., for controlling certain aspects of the mechanical systems  430 - 438 . 
     AV  402  can additionally include a local computing device  410  that is in communication with the sensor systems  404 - 408 , the mechanical systems  430 - 438 , the data center  450 , and the client computing device  470 , among other systems. The local computing device  410  can include one or more processors and memory, including instructions that can be executed by the one or more processors. The instructions can make up one or more software stacks or components responsible for controlling the AV  402 ; communicating with the data center  450 , the client computing device  470 , and other systems; receiving inputs from riders, passengers, and other entities within the AV&#39;s environment; logging metrics collected by the sensor systems  404 - 408 ; and so forth. In this example, the local computing device  410  includes a perception stack  412 , a mapping and localization stack  414 , a planning stack  416 , a control stack  418 , a communications stack  420 , an HD geospatial database  422 , and an AV operational database  424 , among other stacks and systems. 
     Perception stack  412  can enable the AV  402  to “see” (e.g., via cameras, LIDAR sensors, infrared sensors, etc.), “hear” (e.g., via microphones, ultrasonic sensors, RADAR, etc.), and “feel” (e.g., pressure sensors, force sensors, impact sensors, etc.) its environment using information from the sensor systems  404 - 408 , the mapping and localization stack  414 , the HD geospatial database  422 , other components of the AV, and other data sources (e.g., the data center  450 , the client computing device  470 , third-party data sources, etc.). The perception stack  412  can detect and classify objects and determine their current and predicted locations, speeds, directions, and the like. In addition, the perception stack  412  can determine the free space around the AV  402  (e.g., to maintain a safe distance from other objects, change lanes, park the AV, etc.). The perception stack  412  can also identify environmental uncertainties, such as where to look for moving objects, flag areas that may be obscured or blocked from view, and so forth. 
     In some aspects, the perception stack  412  can be configured to perform operations necessary to assigning risk-profile scores to different portions of a drivable roadway area. By way of example, perception stack  412  may be configured to: collect environmental data, via one or more environmental sensors, about an environment around the autonomous vehicle, wherein the environmental data comprises data pertaining to a roadway navigated by the autonomous vehicle and one or more other vehicles navigating the roadway; process the environmental data to generate an area grid comprising one or more grid sections, wherein each grid section corresponds with a different area of the roadway; and determine a risk-profile for the one or more grid sections based on the environmental data. In some aspects, perception stack  412  can be further configured to: resolve a route needed to avoid at least one of the one or more grid sections based on the associated risk-profile. In some aspects, the risk-profile for the one or more grid sections is based on avoidance behaviors associated with the one or more other vehicles navigating the roadway. In some aspects, the risk-profile for the one or more grid sections is based on one or more features associated with the one or more grid sections. In some aspects, the one or more features comprises: a pothole, standing water, debris, or a combination thereof. In some aspects, the one or more features includes a visibility score. 
     Mapping and localization stack  414  can determine the AV&#39;s position and orientation (pose) using different methods from multiple systems (e.g., GPS, IMUs, cameras, LIDAR, RADAR, ultrasonic sensors, the HD geospatial database  422 , etc.). For example, in some embodiments, the AV  402  can compare sensor data captured in real-time by the sensor systems  404 - 408  to data in the HD geospatial database  422  to determine its precise (e.g., accurate to the order of a few centimeters or less) position and orientation. The AV  402  can focus its search based on sensor data from one or more first sensor systems (e.g., GPS) by matching sensor data from one or more second sensor systems (e.g., LIDAR). If the mapping and localization information from one system is unavailable, the AV  402  can use mapping and localization information from a redundant system and/or from remote data sources. 
     The planning stack  416  can determine how to maneuver or operate the AV  402  safely and efficiently in its environment. For example, the planning stack  416  can receive the location, speed, and direction of the AV  402 , geospatial data, data regarding objects sharing the road with the AV  402  (e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., emergency vehicle blaring a siren, intersections, occluded areas, street closures for construction or street repairs, double-parked cars, etc.), traffic rules and other safety standards or practices for the road, user input, and other relevant data for directing the AV  402  from one point to another. The planning stack  416  can determine multiple sets of one or more mechanical operations that the AV  402  can perform (e.g., go straight at a specified rate of acceleration, including maintaining the same speed or decelerating; turn on the left blinker, decelerate if the AV is above a threshold range for turning, and turn left; turn on the right blinker, accelerate if the AV is stopped or below the threshold range for turning, and turn right; decelerate until completely stopped and reverse; etc.), and select the best one to meet changing road conditions and events. If something unexpected happens, the planning stack  416  can select from multiple backup plans to carry out. For example, while preparing to change lanes to turn right at an intersection, another vehicle may aggressively cut into the destination lane, making the lane change unsafe. The planning stack  416  could have already determined an alternative plan for such an event, and upon its occurrence, help to direct the AV  402  to go around the block instead of blocking a current lane while waiting for an opening to change lanes. 
     The control stack  418  can manage the operation of the vehicle propulsion system  430 , the braking system  432 , the steering system  434 , the safety system  436 , and the cabin system  438 . The control stack  418  can receive sensor signals from the sensor systems  404 - 408  as well as communicate with other stacks or components of the local computing device  410  or a remote system (e.g., the data center  450 ) to effectuate operation of the AV  402 . For example, the control stack  418  can implement the final path or actions from the multiple paths or actions provided by the planning stack  416 . This can involve turning the routes and decisions from the planning stack  416  into commands for the actuators that control the AV&#39;s steering, throttle, brake, and drive unit. 
     The communication stack  420  can transmit and receive signals between the various stacks and other components of the AV  402  and between the AV  402 , the data center  450 , the client computing device  470 , and other remote systems. The communication stack  420  can enable the local computing device  410  to exchange information remotely over a network, such as through an antenna array or interface that can provide a metropolitan WIFI network connection, a mobile or cellular network connection (e.g., Third Generation (3G), Fourth Generation (4G), Long-Term Evolution (LTE), 5th Generation (5G), etc.), and/or other wireless network connection (e.g., License Assisted Access (LAA), Citizens Broadband Radio Service (CBRS), MULTEFIRE, etc.). The communication stack  420  can also facilitate local exchange of information, such as through a wired connection (e.g., a user&#39;s mobile computing device docked in an in-car docking station or connected via Universal Serial Bus (USB), etc.) or a local wireless connection (e.g., Wireless Local Area Network (WLAN), Bluetooth®, infrared, etc.). 
     The HD geospatial database  422  can store HD maps and related data of the streets upon which the AV  402  travels. In some embodiments, the HD maps and related data can comprise multiple layers, such as an areas layer, a lanes and boundaries layer, an intersections layer, a traffic controls layer, and so forth. The areas layer can include geospatial information indicating geographic areas that are drivable (e.g., roads, parking areas, shoulders, etc.) or not drivable (e.g., medians, sidewalks, buildings, etc.), drivable areas that constitute links or connections (e.g., drivable areas that form the same road) versus intersections (e.g., drivable areas where two or more roads intersect), and so on. The lanes and boundaries layer can include geospatial information of road lanes (e.g., lane centerline, lane boundaries, type of lane boundaries, etc.) and related attributes (e.g., direction of travel, speed limit, lane type, etc.). The lanes and boundaries layer can also include 3D attributes related to lanes (e.g., slope, elevation, curvature, etc.). The intersections layer can include geospatial information of intersections (e.g., crosswalks, stop lines, turning lane centerlines and/or boundaries, etc.) and related attributes (e.g., permissive, protected/permissive, or protected only left turn lanes; legal or illegal U-turn lanes; permissive or protected only right turn lanes; etc.). The traffic controls lane can include geospatial information of traffic signal lights, traffic signs, and other road objects and related attributes. 
     The AV operational database  424  can store raw AV data generated by the sensor systems  404 - 408  and other components of the AV  402  and/or data received by the AV  402  from remote systems (e.g., the data center  450 , the client computing device  470 , etc.). In some embodiments, the raw AV data can include HD LIDAR point cloud data, image data, RADAR data, GPS data, and other sensor data that the data center  450  can use for creating or updating AV geospatial data as discussed further below with respect to  FIG.  2    and elsewhere in the present disclosure. 
     The data center  450  can be a private cloud (e.g., an enterprise network, a co-location provider network, etc.), a public cloud (e.g., an Infrastructure as a Service (IaaS) network, a Platform as a Service (PaaS) network, a Software as a Service (SaaS) network, or other Cloud Service Provider (CSP) network), a hybrid cloud, a multi-cloud, and so forth. The data center  450  can include one or more computing devices remote to the local computing device  410  for managing a fleet of AVs and AV-related services. For example, in addition to managing the AV  402 , the data center  450  may also support a ridesharing service, a delivery service, a remote/roadside assistance service, street services (e.g., street mapping, street patrol, street cleaning, street metering, parking reservation, etc.), and the like. 
     The data center  450  can send and receive various signals to and from the AV  402  and client computing device  470 . These signals can include sensor data captured by the sensor systems  404 - 408 , roadside assistance requests, software updates, ridesharing pick-up and drop-off instructions, and so forth. In this example, the data center  450  includes a data management platform  452 , an Artificial Intelligence/Machine Learning (AI/ML) platform  454 , a simulation platform  456 , a remote assistance platform  458 , a ridesharing platform  460 , and map management system platform  462 , among other systems. 
     Data management platform  452  can be a “big data” system capable of receiving and transmitting data at high velocities (e.g., near real-time or real-time), processing a large variety of data, and storing large volumes of data (e.g., terabytes, petabytes, or more of data). The varieties of data can include data having different structure (e.g., structured, semi-structured, unstructured, etc.), data of different types (e.g., sensor data, mechanical system data, ridesharing service, map data, audio, video, etc.), data associated with different types of data stores (e.g., relational databases, key-value stores, document databases, graph databases, column-family databases, data analytic stores, search engine databases, time series databases, object stores, file systems, etc.), data originating from different sources (e.g., AVs, enterprise systems, social networks, etc.), data having different rates of change (e.g., batch, streaming, etc.), or data having other heterogeneous characteristics. The various platforms and systems of the data center  450  can access data stored by the data management platform  452  to provide their respective services. 
     The AI/ML platform  454  can provide the infrastructure for training and evaluating machine learning algorithms for operating the AV  402 , the simulation platform  456 , the remote assistance platform  458 , the ridesharing platform  460 , the map management system platform  462 , and other platforms and systems. Using the AI/ML platform  454 , data scientists can prepare data sets from the data management platform  452 ; select, design, and train machine learning models; evaluate, refine, and deploy the models; maintain, monitor, and retrain the models; and so on. 
     The simulation platform  456  can enable testing and validation of the algorithms, machine learning models, neural networks, and other development efforts for the AV  402 , the remote assistance platform  458 , the ridesharing platform  460 , the map management system platform  462 , and other platforms and systems. The simulation platform  456  can replicate a variety of driving environments and/or reproduce real-world scenarios from data captured by the AV  402 , including rendering geospatial information and road infrastructure (e.g., streets, lanes, crosswalks, traffic lights, stop signs, etc.) obtained from the map management system platform  462 ; modeling the behavior of other vehicles, bicycles, pedestrians, and other dynamic elements; simulating inclement weather conditions, different traffic scenarios; and so on. 
     The remote assistance platform  458  can generate and transmit instructions regarding the operation of the AV  402 . For example, in response to an output of the AI/ML platform  454  or other system of the data center  450 , the remote assistance platform  458  can prepare instructions for one or more stacks or other components of the AV  402 . 
     The ridesharing platform  460  can interact with a customer of a ridesharing service via a ridesharing application  472  executing on the client computing device  470 . The client computing device  470  can be any type of computing system, including a server, desktop computer, laptop, tablet, smartphone, smart wearable device (e.g., smart watch, smart eyeglasses or other Head-Mounted Display (HMD), smart ear pods or other smart in-ear, on-ear, or over-ear device, etc.), gaming system, or other general purpose computing device for accessing the ridesharing application  472 . The client computing device  470  can be a customer&#39;s mobile computing device or a computing device integrated with the AV  402  (e.g., the local computing device  410 ). The ridesharing platform  460  can receive requests to be picked up or dropped off from the ridesharing application  472  and dispatch the AV  402  for the trip. 
     Map management system platform  462  can provide a set of tools for the manipulation and management of geographic and spatial (geospatial) and related attribute data. The data management platform  452  can receive LIDAR point cloud data, image data (e.g., still image, video, etc.), RADAR data, GPS data, and other sensor data (e.g., raw data) from one or more AVs  402 , UAVs, satellites, third-party mapping services, and other sources of geospatially referenced data. The raw data can be processed, and map management system platform  462  can render base representations (e.g., tiles (2D), bounding volumes (3D), etc.) of the AV geospatial data to enable users to view, query, label, edit, and otherwise interact with the data. Map management system platform  462  can manage workflows and tasks for operating on the AV geospatial data. Map management system platform  462  can control access to the AV geospatial data, including granting or limiting access to the AV geospatial data based on user-based, role-based, group-based, task-based, and other attribute-based access control mechanisms. Map management system platform  462  can provide version control for the AV geospatial data, such as to track specific changes that (human or machine) map editors have made to the data and to revert changes when necessary. Map management system platform  462  can administer release management of the AV geospatial data, including distributing suitable iterations of the data to different users, computing devices, AVs, and other consumers of HD maps. Map management system platform  462  can provide analytics regarding the AV geospatial data and related data, such as to generate insights relating to the throughput and quality of mapping tasks. 
     In some embodiments, the map viewing services of map management system platform  462  can be modularized and deployed as part of one or more of the platforms and systems of the data center  450 . For example, the AI/ML platform  454  may incorporate the map viewing services for visualizing the effectiveness of various object detection or object classification models, the simulation platform  456  may incorporate the map viewing services for recreating and visualizing certain driving scenarios, the remote assistance platform  458  may incorporate the map viewing services for replaying traffic incidents to facilitate and coordinate aid, the ridesharing platform  460  may incorporate the map viewing services into the client application  472  to enable passengers to view the AV  402  in transit en route to a pick-up or drop-off location, and so on. 
       FIG.  5    illustrates an example processor-based system with which some aspects of the subject technology can be implemented. For example, processor-based system  500  can be any computing device making up internal computing system  510 , remote computing system  550 , a passenger device executing the rideshare app  570 , internal computing device  530 , or any component thereof in which the components of the system are in communication with each other using connection  505 . Connection  505  can be a physical connection via a bus, or a direct connection into processor  510 , such as in a chipset architecture. Connection  505  can also be a virtual connection, networked connection, or logical connection. 
     In some embodiments, computing system  500  is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some embodiments, the components can be physical or virtual devices. 
     Example system  500  includes at least one processing unit (CPU or processor)  510  and connection  505  that couples various system components including system memory  515 , such as read-only memory (ROM)  520  and random-access memory (RAM)  525  to processor  510 . Computing system  500  can include a cache of high-speed memory  512  connected directly with, in close proximity to, or integrated as part of processor  510 . 
     Processor  510  can include any general-purpose processor and a hardware service or software service, such as services  532 ,  534 , and  536  stored in storage device  530 , configured to control processor  510  as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor  510  may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     To enable user interaction, computing system  500  includes an input device  545 , which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system  500  can also include output device  535 , which can be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system  500 . Computing system  500  can include communications interface  540 , which can generally govern and manage the user input and system output. The communication interface may perform or facilitate receipt and/or transmission wired or wireless communications via wired and/or wireless transceivers, including those making use of an audio jack/plug, a microphone jack/plug, a universal serial bus (USB) port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, a proprietary wired port/plug, a BLUETOOTH® wireless signal transfer, a BLUETOOTH® low energy (BLE) wireless signal transfer, an IBEACON® wireless signal transfer, a radio-frequency identification (RFID) wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR) communication wireless signal transfer, Public Switched Telephone Network (PSTN) signal transfer, Integrated Services Digital Network (ISDN) signal transfer, 3G/4G/5G/LTE cellular data network wireless signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. 
     Communication interface  540  may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers that are used to determine a location of the computing system  500  based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based Global Positioning System (GPS), the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. 
     Storage device  530  can be a non-volatile and/or non-transitory and/or computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, any other magnetic storage medium, flash memory, memristor memory, any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick® card, a smartcard chip, a EMV chip, a subscriber identity module (SIM) card, a mini/micro/nano/pico SIM card, another integrated circuit (IC) chip/card, random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L #), resistive random-access memory (RRAM/ReRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and/or a combination thereof. 
     Storage device  530  can include software services, servers, services, etc., that when the code that defines such software is executed by the processor  510 , it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor  510 , connection  505 , output device  535 , etc., to carry out the function. 
     As understood by those of skill in the art, machine-learning based classification techniques can vary depending on the desired implementation. For example, machine-learning classification schemes can utilize one or more of the following, alone or in combination: hidden Markov models; recurrent neural networks; convolutional neural networks (CNNs); deep learning; Bayesian symbolic methods; general adversarial networks (GANs); support vector machines; image registration methods; applicable rule-based system. Where regression algorithms are used, they may include including but are not limited to: a Stochastic Gradient Descent Regressor, and/or a Passive Aggressive Regressor, etc. 
     Machine learning classification models can also be based on clustering algorithms (e.g., a Mini-batch K-means clustering algorithm), a recommendation algorithm (e.g., a Miniwise Hashing algorithm, or Euclidean Locality-Sensitive Hashing (LSH) algorithm), and/or an anomaly detection algorithm, such as a Local outlier factor. Additionally, machine-learning models can employ a dimensionality reduction approach, such as, one or more of: a Mini-batch Dictionary Learning algorithm, an Incremental Principal Component Analysis (PCA) algorithm, a Latent Dirichlet Allocation algorithm, and/or a Mini-batch K-means algorithm, etc. 
     Embodiments within the scope of the present disclosure may also include tangible and/or non-transitory computer-readable storage media or devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices can be any available device that can be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which can be used to carry or store desired program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network or another communications connection (either hardwired, wireless, or combination thereof) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable storage devices. 
     Computer-executable instructions include, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform tasks or implement abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps. 
     Other embodiments of the disclosure may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
     The various embodiments described above are provided by way of illustration only and should not be construed to limit the scope of the disclosure. For example, the principles herein apply equally to optimization as well as general improvements. Various modifications and changes may be made to the principles described herein without following the example embodiments and applications illustrated and described herein, and without departing from the spirit and scope of the disclosure. Claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim.