Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.

Abstract: Various technologies described herein pertain to routing an autonomous vehicle based upon risk of takeover of the autonomous vehicle by a human operator. A computing system receives an origin location and a destination location of the autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a computer-implemented model. The computer-implemented model is generated based upon labeled data indicative of instances in which autonomous vehicles are observed to transition from operating autonomously to operating based upon conduction by human operators while the autonomous vehicles are executing predefined maneuvers. The computer-implemented model takes, as input, an indication of a maneuver in the predefined maneuvers that is performed by the autonomous vehicle when the autonomous vehicle follows a candidate route.

Abstract: Various technologies described herein pertain to autonomous vehicle consumption of real-time public transportation data to guide curb access and usage. An autonomous vehicle receives a trip request for a ride specifying a requested pullover location. The autonomous vehicle receives public transportation data specifying an expected arrival time of a public transportation vehicle at a reserved zone within proximity of the requested pullover location. The autonomous vehicle evaluates availability of the reserved zone during an expected occupancy time of the reserved zone by the autonomous vehicle based on the expected arrival time of the public transportation vehicle at the reserved zone. The autonomous vehicle selects an actual pullover location for the ride in the autonomous vehicle based on the availability of the reserved zone during the expected occupancy time. The autonomous vehicle stops at the actual pullover location for the ride in the autonomous vehicle.

Abstract: Systems, methods, and devices are disclosed for synchronizing map updates for a navigating autonomous vehicle. A management service on a managing node can receive an update to a portion of a first map, where a limited visibility map, which is a subset of the first map, can be determined based on a geographical area around an autonomous vehicle navigating a route. The limited visibility map can be synchronized among all the nodes while the autonomous vehicle is using the limited visibility map by the management service delivering, to a plurality of downstream nodes, the update to the limited visibility map when the update does not modify a portion of the limited visibility map, and by bypassing, to the plurality of downstream nodes, the update to the limited visibility map when the update modifies a portion of the limited visibility map.

Abstract: Disclosed are systems and methods for specifying goals and behavioral parameters for planning systems of autonomous vehicles. In some aspects, a method includes receiving, by a mission manager in an autonomous vehicle (AV), a mission from an input source of the AV, the mission comprising a request for a task that the AV is to fulfill; deconflicting the mission with one or more other missions of the AV to generate a ranked list of missions; selecting a target mission from the ranked list of missions in accordance with priorities corresponding to each mission in the ranked list of missions; generating one or more scenarios based on the target mission, the one or more scenarios comprising encoded representations of local and geometric terms to cause the target mission to be fulfilled by the AV; and dispatching the one or more scenarios to a planner layer of the AV.

Abstract: Systems, methods, and devices are disclosed for synchronizing map updates for a navigating autonomous vehicle. A management service on a managing node can receive an update to a portion of a first map, where a limited visibility map, which is a subset of the first map, can be determined based on a geographical area around an autonomous vehicle navigating a route. The limited visibility map can be synchronized among all the nodes while the autonomous vehicle is using the limited visibility map by the management service delivering, to a plurality of downstream nodes, the update to the limited visibility map when the update does not modify a portion of the limited visibility map, and by bypassing, to the plurality of downstream nodes, the update to the limited visibility map when the update modifies a portion of the limited visibility map.

Abstract: Disclosed are systems and methods for responding to detected emergency vehicles. In some aspects, a method includes generating an emergency vehicle (EMV) detection signal based on sensor signals of an autonomous vehicle (AV); determining, based on the EMV detection signal, whether remote assistance (RA) is invoked for the EMV signal or whether the AV is to handle the EMV detection signal autonomously; and responsive to determining that the AV is to handle the EMV detection signal autonomously, determining a trajectory of the AV in response to the EMV detection signal.

Abstract: Disclosed are systems and methods for responding to unsupported or degraded conditions for autonomous vehicles (AVs). In some aspects, a method includes aggregating one or more trigger events generated across a software stack of an autonomous vehicle (AV) as aggregated trigger events, the one or more trigger events indicating at least one of unsupported or degraded conditions that bring the AV outside of allowable driving conditions of the AV, ranking the aggregated trigger events in accordance with priority levels corresponding to the aggregated trigger events, determining an AV maneuver to respond to a selected trigger event, wherein the selected trigger event is a highest ranked trigger event of the aggregated trigger events, causing the AV maneuver to be performed by the AV, and determining whether to at least one of fail the AV or recover the AV to a normal driving state in response to the selected trigger event.

Abstract: For one embodiment of the present disclosure, systems and methods for dynamically responding to detected changes in a semantic map of an autonomous vehicle (AV) are described. A computer implemented method includes obtaining sensor signals from a sensor system of the AV to monitor driving operations, processing the sensor signals for sensor observations of the sensor system, determining whether a map change exists between the sensor observations and a prerecorded semantic map, determining whether the map change is located in a planned route of the AV when the map change exists, and generating a first scenario with a first priority level to to stop the AV at a safe location based on a location of the map change.

Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A routing system generates an initial route from a source location to a target location irrespective of avoidance areas included on the initial route. When a number of avoidance areas on the initial route exceed a predetermined threshold, one or more alternative routes are generated from the source location to the target location that respectively circumvent a corresponding identified avoidance area on the initial route. Metrics are evaluated for the one or more alternative routes and a subset of avoidance areas are outputted to desirably be removed from the set of avoidance areas.

Abstract: The subject disclosure relates to techniques for real-time lane validation. A process of the disclosed technologies can include steps for receiving a route from a remote computing system, the route indicating lanes on the route that an autonomous vehicle must traverse, traversing at least one lane of the lanes on the route, and sending sensor data to the remote computing system after traversing the at least one lane, the sensor data indicating whether the at least one lane is in accordance with a virtual lane of a virtual map, the virtual lane corresponding to the at least one lane.

Abstract: The subject disclosure relates to techniques for real-time lane validation. A process of the disclosed technologies can include steps for receiving a route from a remote computing system, the route indicating lanes on the route that an autonomous vehicle must traverse, traversing at least one lane of the lanes on the route, and sending sensor data to the remote computing system after traversing the at least one lane, the sensor data indicating whether the at least one lane is in accordance with a virtual lane of a virtual map, the virtual lane corresponding to the at least one lane.

Abstract: Various technologies described herein pertain to autonomous vehicle consumption of real-time public transportation data to guide curb access and usage. An autonomous vehicle receives a trip request for a ride specifying a requested pullover location. The autonomous vehicle receives public transportation data specifying an expected arrival time of a public transportation vehicle at a reserved zone within proximity of the requested pullover location. The autonomous vehicle evaluates availability of the reserved zone during an expected occupancy time of the reserved zone by the autonomous vehicle based on the expected arrival time of the public transportation vehicle at the reserved zone. The autonomous vehicle selects an actual pullover location for the ride in the autonomous vehicle based on the availability of the reserved zone during the expected occupancy time. The autonomous vehicle stops at the actual pullover location for the ride in the autonomous vehicle.

Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.

Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.

Abstract: Various technologies described herein pertain to routing an autonomous vehicle based upon risk of takeover of the autonomous vehicle by a human operator. A computing system receives an origin location and a destination location of the autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a computer-implemented model. The computer-implemented model is generated based upon labeled data indicative of instances in which autonomous vehicles are observed to transition from operating autonomously to operating based upon conduction by human operators while the autonomous vehicles are executing predefined maneuvers. The computer-implemented model takes, as input, an indication of a maneuver in the predefined maneuvers that is performed by the autonomous vehicle when the autonomous vehicle follows a candidate route.

Abstract: Systems and methods are provided for accelerating autonomous vehicle testing. In particular, autonomous vehicle testing is accelerated by providing proactive waypoints. Systems and methods are provided for proactively seeking out times and locations in which an autonomous vehicle will most likely encounter on-road exposure of a particular set of variables. By increasing on-road exposure to a particular set of variables, an autonomous vehicle's response to the set of variables be more efficiently tested. According to various implementations, autonomous vehicles are automatically dispatched to routed to areas in which a test criteria frequently occurs, during particular times when the test criteria is likely to occur.

Abstract: Various technologies described herein pertain to routing an autonomous vehicle based upon risk of takeover of the autonomous vehicle by a human operator. A computing system receives an origin location and a destination location of the autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a computer-implemented model. The computer-implemented model is generated based upon labeled data indicative of instances in which autonomous vehicles are observed to transition from operating autonomously to operating based upon conduction by human operators while the autonomous vehicles are executing predefined maneuvers. The computer-implemented model takes, as input, an indication of a maneuver in the predefined maneuvers that is performed by the autonomous vehicle when the autonomous vehicle follows a candidate route.

Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A routing system generates an initial route from a source location to a target location irrespective of avoidance areas included on the initial route. When a number of avoidance areas on the initial route exceed a predetermined threshold, one or more alternative routes are generated from the source location to the target location that respectively circumvent a corresponding identified avoidance area on the initial route. Metrics are evaluated for the one or more alternative routes and a subset of avoidance areas are outputted to desirably be removed from the set of avoidance areas.

Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A routing system generates an initial route from a source location to a target location irrespective of avoidance areas included on the initial route. When a number of avoidance areas on the initial route exceed a predetermined threshold, one or more alternative routes are generated from the source location to the target location that respectively circumvent a corresponding identified avoidance area on the initial route. Metrics are evaluated for the one or more alternative routes and a subset of avoidance areas are outputted to desirably be removed from the set of avoidance areas.