Uatc, LLC

Uber Technologies, Inc.

Aurora Operations, Inc.

Bryan John Nagy

Brett Bavar

Xiaodong Zhang

Adam Cole Panzica

Anthony Joseph Stentz

Robert Michael S Dean

Brett Browning

Eric Michael Perko

Adam Henry Polk Milstein

Robert Zlot

Peter W Rander

David Prasser

Michael Voznesensky

Marcial Hernandez

Julie Derence

Tashwin Khurana

Jordan Romaidis

Adrian Rechy Romero

Matthew Sweeney

Ethan Eade

Molly Castle Nix

Brent Justin Goldman

Richard Brian Donnelly

Peter Hansen

Donald Jason Burnette

Colin Jeffrey Green

Emily Bartel

Jeff Schneider

Xxx Xinjilefu

Lisa Christine Weitekamp

Yifang Liu

Andrew Raymond Sturges

Gordon Peter Bailey

Alexander Edward Chao

Collin Christopher Otis

Jian Wen

Jacob Robert Forster

Robert Dean

Mark Yen

Alan B Sheinberg

Austin Thomas

Jennifer Joyce Padgett

Evan Herbst

Michael Napoli

Yanbo Zhao

Misna Sameer

Shubhit Mohan Singh

Brent Goldman

Peter Rander

Molly Nix

Various examples are directed to systems and methods for routing a plurality of autonomous vehicles. A batch routing system may receive a first general route cost request from a first autonomous vehicle. The batch routing system may determine a first set of general routes for the first autonomous vehicle. The batch routing system may send a first general route cost reply to the first autonomous vehicle. The first general route cost reply may indicate a first set of costs associated with the first set of general routes. The first set of costs may comprise a first general cost for the first general route and a second general cost for the second general route.

Routing multiple autonomous vehicles using local and general route planning

Systems and methods herein describe accessing a set of feature flags, a first feature flag in the set of feature flag describing vehicle navigation behavior on a routing graph, the routing graph representing the roadway, associating the set of feature flags with a vehicle, applying the associated set of feature flags to a graph traversal algorithm to generate a modified graph traversal algorithm, generating the route using the modified graph traversal algorithm; and transmitting instructions to the vehicle to begin executing the route.

Routing feature flags

Examples described herein are directed to systems and methods for autonomous vehicle control system testing. A testing utility may receive log data describing a first location of a capturing vehicle at a first driven route time and a second location of the capturing vehicle at a second drive route time. The testing utility may, using at least the location data, determine a first routing graph modification to constrain a navigator component of an autonomous vehicle control system under test. The testing utility may provide the first routing graph modification to the navigator component to cause the navigator component to generate a testing route.

Autonomous vehicle control system testing

An autonomous vehicle can access map data comprising travel ways within a surrounding environment of the autonomous vehicle, and further access constraint data that define original navigational constraints within the map data. The vehicle can further receive constraint files comprising additional navigational constraints within the map data and modify the constraint data based on the constraint files. The vehicle can determine a travel route to a destination using composite constraint data and autonomously driver to the destination along the travel route accordingly.

Navigational constraints for autonomous vehicles

An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.

Determining autonomous vehicle routes

Various examples are directed to systems and methods for routing an autonomous vehicle. A vehicle autonomy system may generate first route data describing a first route for the autonomous vehicle to a first target location and control the autonomous vehicle using the first route data. The vehicle autonomy system may determine that the autonomous vehicle is within a threshold of the first target location and select a second target location associated with at least a second stopping location. The vehicle autonomy system may generate second route data describing a route extension of the first route from the first target location to the second target location and control the autonomous vehicle using the second route data.

Autonomous vehicle routing with route extension

Various examples are directed to routing autonomous vehicles. A processor unit accesses first routing graph modification data and second routing graph modification data. The first routing graph modification data based at least in part on first vehicle capability data describing a first type of autonomous vehicle and the second routing graph modification data based at least in part on second vehicle capability data describing a second type of autonomous vehicle. The processor unit accesses routing graph data describing a plurality of graph elements and generates a first route for a first autonomous vehicle of the first type based at least in part on the first routing graph modification data and the routing graph data. The processor unit also generates a second route for a second autonomous vehicle of the second type based at least in part on the second routing graph modification data and the routing graph data.

Routing graph management in autonomous vehicle routing

A method for receiving autonomous vehicle (AV) map data associated with an AV map of a geographic location and coverage map data associated with a coverage map of the geographic location. The AV map data is associated with an AV lane of a roadway in the geographic location, and the coverage map data is associated with a coverage lane of the roadway in the geographic location. The method includes generating a hybrid map of the geographic location based on the AV map data and the coverage map data and providing hybrid map data associated with the hybrid map for routing of an AV. The hybrid map includes the AV lane linked with the coverage lane of the roadway.

Map creation from hybrid data

A method for receiving autonomous vehicle (AV) driving path data associated with a driving path in a roadway of a geographic location. The driving path data associated with a trajectory for an AV in a roadway and trajectory points in a trajectory of the driving path in the roadway for determining at least one feature of the roadway positioned a lateral distance from a first trajectory of the one or more trajectories of the driving path of an AV based on the map data. The method includes receiving map data associated with a map of a geographic location, determining a driving path for an AV in a roadway, generating driving path information based on a trajectory point in a trajectory of the driving path, and providing driving path data associated with the driving path to an AV for controlling the AV on the roadway.

Mapped driving paths for autonomous vehicle

Various examples are directed to systems and methods for navigating an autonomous vehicle. Trip plan data may describe a plurality of candidate vehicle start points, a plurality of candidate waypoints, and a plurality of candidate vehicle end points. A plurality of candidate routes may be determined between an algorithm start point and an algorithm end point. Each candidate route of the plurality of candidate routes may include at least one of the plurality of candidate waypoints and at least one of the plurality of candidate vehicle end points. A best rate may be determined using the plurality of candidate routes. The best route may include a first candidate vehicle start point, a first candidate waypoint, and a first candidate vehicle end point. The autonomous vehicle may be controlled along the best route from the first candidate vehicle start point towards the first candidate vehicle end point.

Autonomous vehicle waypoint routing

Various examples are directed to systems and methods for controlling an autonomous vehicle. For example, a navigator system at an autonomous vehicle may generate a plurality of local routes beginning at a vehicle location and extending to a plurality of local route end points. The navigator system may access general route cost data, the general route cost data describing general route costs from the plurality of local route end points to a trip end point. The navigator system may select the first local route of the plurality of routes based at least in part on the general route cost data. A vehicle autonomy system at the autonomous vehicle may begin to control the autonomous vehicle along the first local route.

Autonomous vehicle routing with local and general routes

A georeferenced trajectory system for vehicles receives trajectory data generated by a plurality of vehicle sensors and scaffolds of previously generated maps and aligns geometry data for a geographic region and trajectory data from the received data from different map builds. A scaffold of a geographic region to be mapped during an initial map build is generated, and the trajectory data from respective map builds is aligned with the scaffold of previously generated maps to generate a map of the geographic region. The resulting map expands the coverage of the existing map such that old and new map data is in a common consistent reference frame whereby the map may be built incrementally by merging or expanding local scaffolds and filling in the merged or expanded scaffold while ensuring global consistency.

Scaffolds for globally consistent maps

An autonomous vehicle (AV) includes a vehicle computing system including one or more processors programmed to receive map data associated with a map of a geographic location, determine, based on the map data, one or more local routes in the one or more roadways between the current location of the AV and one or more exit locations, and control travel of the AV based on a selected local route of the one or more local routes. The map includes one or more roadways in the geographic location. The map data includes a global route in the one or more roadways between a current location of the AV and a destination location of the AV. The one or more exit locations are located between the current location of the AV and the destination location of the AV.

Global and local navigation for self-driving

A computing system can generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles (AVs). The map constraint interface can comprise a unified document model enabling the fleet operator to configure a set of constraint layers of autonomy maps utilized by the AVs. Each constraint layer can include a toggle feature that enables the fleet operator to enable and disable the constraint layer. The system can receive, via the map constraint interface, a set of inputs configuring the set of constraint layers of the one or more autonomy maps, compile a set of updated map constraints, corresponding to the configured set of constraint layers, into a document container, and output the document container to a subset of the AVs to enable the subset of AVs to integrate the set of updated map constraints with the autonomy maps.

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Allison Park, PA, United States of America

Bryan John Nagy