Uatc, LLC

Uber Technologies, Inc.

Adam Cole Panzica

Brett Bavar

Bryan John Nagy

Xiaodong Zhang

Ethan Eade

Anthony Joseph Stentz

Brett Browning

Peter Hansen

Eric Michael Perko

Robert Zlot

David Prasser

Xxx Xinjilefu

Adam Henry Polk Milstein

Robert Michael S Dean

Adrian Rechy Romero

Robert Dean

Gordon Peter Bailey

Brent Justin Goldman

Mark Yen

Michael Voznesensky

Marcial Hernandez

Tashwin Khurana

Jordan Romaidis

Julie Derence

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

An autonomous vehicle (AV) includes a vehicle computing system configured to receive map data of a geographic location, obtain position estimate data of the autonomous vehicle and determine a route of the autonomous vehicle including a plurality of roadways in the plurality of submaps. The autonomous vehicle determines a route including a plurality of roadways, determines a first roadway in the plurality of roadways closest to the position estimate and a second roadway outside the plurality of roadways closest to the position estimate of the autonomous vehicle, and determines a pose relative to the first roadway or the second roadway based on a distance between the position estimate of the autonomous vehicle and a roadway associated with a prior pose of the autonomous vehicle to control travel of the autonomous vehicle based on the vehicle pose.

Low quality pose lane associator

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 method of controlling navigation of autonomous vehicles includes accessing map data descriptive of the identity and location of different travel ways within a surrounding environment of an autonomous vehicle and accessing constraint data descriptive of one or more geographic areas or geographic identifiers, within the map data, for which associated navigational constraints are defined. The constraint data includes a depart constraint that specifies an area that an autonomous vehicle may not enter but may exit if inside the area when the depart constraint is imposed, thereby preventing the autonomous vehicle from being trapped in a forbidden area even though the autonomous vehicle may safely complete its route. A travel route is determined for the autonomous vehicle based at least in part on the map data evaluated relative to the constraint data including the depart constraint, and motion of the autonomous vehicle is controlled based on the determined travel route.

Depart constraints implementation in autonomous vehicle routing

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.

Navigational constraints for autonomous vehicles

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, including, one or more local routes in the one or more roadways between the current location of the AV and one or more exit locations, receive sensor data associated with an object detected in an environment surrounding the AV, select a local route of the one or more local routes based on the sensor data 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 one or more exit locations are located between the current location of the AV and the destination location of the AV in a global route in the one or more roadways.

A control system for an autonomous vehicle can determine a risk value for each respective path segment of a plurality of path segments in a given area that includes a destination of the autonomous vehicle. The risk value can correspond to a cost layer in a map that includes the respective path segment. Based on the risk value for each respective path segment, the control system can determine a travel route for the autonomous vehicle to the destination, and autonomously control the autonomous vehicle to navigate along the travel route to the destination.

Autonomous vehicle routing using annotated maps

An autonomous vehicle (AV) includes a vehicle computing system including one or more processors configured to receive map data associated with a map of a geographic location, receive pose data based on a pose estimate associated with a location of the autonomous vehicle, determine, based on the pose data, that the autonomous vehicle is on a coverage lane, and, in response to determining that the autonomous vehicle is on the coverage lane, determine one or more candidate lanes, generate a route plan, based on the one or more candidate lanes and a current lane, and control travel of the autonomous vehicle on the route plan. The map includes (i) a coverage lane where the autonomous vehicle can travel under a partially-autonomous mode or a manual mode, and (ii) an AV lane where the autonomous vehicle can travel under a fully-autonomous mode. The autonomous vehicle configured to determine a pose estimate in a submap of a plurality of submaps where an autonomous vehicle can travel.

Low quality pose

A system and method for autonomous vehicle routing using annotated maps. For at least some path segments within a geographic region in which the vehicle is operating, values are determined for the path segments based at least on risk factors associated with autonomous operation of the vehicle along each path segment. Path segments are combined to generate a travel route, from a first location to a second location, based on the determined values, and the vehicle is controlled to navigate along the travel route.

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Pittsburgh, PA, United States of America

Adam Cole Panzica