Uatc, LLC

Uber Technologies, Inc.

Brett Bavar

Bryan John Nagy

Adam Cole Panzica

Xiaodong Zhang

Marcial Hernandez

Tashwin Khurana

Jordan Romaidis

Julie Derence

Matthew Sweeney

Ethan Eade

Molly Castle Nix

Anthony Joseph Stentz

Brett Browning

Peter Hansen

Eric Michael Perko

Colin Jeffrey Green

Emily Bartel

Robert Zlot

David Prasser

Xxx Xinjilefu

Adam Henry Polk Milstein

Robert Michael S Dean

Robert Dean

Gordon Peter Bailey

Adrian Rechy Romero

Misna Sameer

Molly Nix

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

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

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

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

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.

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

Systems and methods for controlling the motion of an autonomous vehicle are provided. In one example embodiment, one or more computing devices on-board an autonomous vehicle receive one or more constraint files including constraint data descriptive of one or more geographic identifiers (e.g., a polygon) and an application type (e.g., partial inclusion, complete inclusion, partial exclusion, complete exclusion) associated with each of the one or more geographic identifiers. Map data descriptive of the identity and location of different travel ways within the surrounding environment of the autonomous vehicle is accessed. A travel route for navigating the autonomous vehicle is determined, wherein the travel route is determined at least in part from the map data evaluated relative to the constraint data. Motion of the autonomous vehicle can be controlled based at least in part on the determined travel route.

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.

A hybrid trip planning system can receive transport requests from requesting users, and determine a pick-up location from each transport request. The pick-up location can be within or external to an autonomy grid on which a plurality of autonomous vehicles (AVs) can operate in an autonomous mode. The system can further determine a drop-off location from the transport request, the drop-off location also being within or external to the autonomy grid. The system can select and route an AV to a most optimal entry and/or exit point of the autonomy grid based on the transport request, where the selected AV is to switch between a manual and an autonomous mode.

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Pittsburgh, PA, United States of America

Brett Bavar