Gm Cruise Holdings LLC

Lucio Otavio Marchioro Rech

Andrew Robinson

Nitin Kumar Passa

Brian Donohue

Laura Athena Freeman

Jeremy Allan

Geoffrey Louis Chi-Johnston

Vishal Suresh Vaingankar

Antony Joseph

Mason Swofford

Robin Yang

Abhishek Jain

Hooman Barekatain

Roman Sergeev

Saranya Konala

George Herbert Hines

Ryan Zelen

Sean Gregory Skwerer

Kyle Liang

Alexander John Bowe

Kavya Sambana

Erin Antcliffe

Tucker Allen Paxton

Anav Sood

Alex Bowe

Yang Tian

Mikhail Chetin

Beini Jiang

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.

Responding to unsupported or degraded conditions for autonomous vehicles

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.

Semantic map with live updates

The subject disclosure relates to solutions for arbitrating traffic blockages along vehicle routes. In some aspects, the disclosed technology encompasses a method including steps for determining a vehicle route, wherein the vehicle route comprises at least one lane plan to be followed by an autonomous vehicle (AV), identifying a blockage along the vehicle route, analyzing the blockage to determine if the blockage constitutes a persistent blockage, and if the blockage constitutes a persistent blockage, associating a time value with the blockage. Systems and machine-readable media are also provided.

Blockage routing and maneuver arbitration

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.

System and method for real-time lane validation

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.

Systems and methods for overlap-aware ranking of navigation avoidance areas for autonomous vehicles

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.

Autonomous vehicle consumption of real-time public transportation data to guide curb access and usage

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. The autonomous vehicle then follows the route from the origin location to the destination location.

Autonomous vehicle routing based upon risk of autonomous vehicle takeover

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.

Analysis of network effects of avoidance areas on routing

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San Mateo, CA, United States of America

Lucio Otavio Marchioro Rech