Uatc, LLC

Aurora Operations, Inc.

Eric Michael Perko

Michael Lee Phillips

Samuel Philip Marden

David McAllister Bradley

Bryan John Nagy

Colin Jeffrey Green

Kalin Vasilev Gochev

Anthony Joseph Stentz

Brian C Becker

Narek Melik-Barkhudarov

Moslem Kazemi

Wei Liu

Eric Lloyd Wilkinson

Yang Xu

Don Burnette

Somchaya Liemhetcharat

Harishma Dayanidhi

Donald Jason Burnette

Brett Bavar

Xiaodong Zhang

Adam Cole Panzica

Lisa Christine Weitekamp

Adam Henry Polk Milstein

Robert Michael S Dean

Edward Henry Lim

Nicholas Fermin Andrey Sterner

Chenggang Liu

Peter W Rander

Daoyuan Jia

Jessica Elizabeth Smith

Utku Eren

Randy Warner

Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.

Systems and methods for autonomous vehicle controls

The present disclosure is directed to deviating from a planned path for an autonomous vehicle. In particular, a computing system comprising one or more computing devices physically located onboard an autonomous vehicle can identify one or more boundaries at least in part defining a lane in which the autonomous vehicle is traveling along a path of a planned route. Responsive to identifying one or more obstructions ahead of the autonomous vehicle along the path, the computing system can: determine one or more deviations from the path that would result in the autonomous vehicle avoiding the obstruction(s) and at least partially crossing at least one of the one or more boundaries; and generate, based at least in part on the deviation(s), a motion plan instructing the autonomous vehicle to deviate from the path such that it avoids the obstruction(s) and continues traveling along the planned route.

Methods and systems for autonomous vehicle motion deviation

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that generates constraints as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can generate constraints based on where objects of interest are predicted to be relative to an autonomous vehicle. A constraint solver can identify navigation decisions for each of the constraints that provide a consistent solution across all constraints. The solution provided by the constraint solver can be in the form of a trajectory path determined relative to constraint areas for all objects of interest. The trajectory path represents a set of navigation decisions such that a navigation decision relative to one constraint doesn't sacrifice an ability to satisfy a different navigation decision relative to one or more other constraints.

Discrete decision architecture for motion planning system of an autonomous vehicle

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

The present disclosure is directed generating candidate trajectories and selecting one of them to be implemented. In particular, a computing system can obtain an initial travel path for an autonomous vehicle. The computing system can obtain sensor data describing objects within an environment of the autonomous vehicle. The computing system can generate a plurality of trajectories for the autonomous vehicle based on the sensor data and the initial travel path. The computing system can determine whether the initial travel path, an offset profile, and a velocity profile meet flatness criteria. In response to determining that the initial travel path, the offset profile, and the velocity profile meet the flatness criteria, the computing system can combine the initial travel path, the offset profile, and the velocity profile into the respective trajectory. The computing system can determine a trajectory from the plurality of trajectories.

Method for using lateral motion to optimize trajectories 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, 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

The present disclosure provides autonomous vehicles that include a vehicle intention system that provides intention signals indicative of an intention of the autonomous vehicle. In particular, in one example, the vehicle intention system can obtain one or more operational messages from various systems or components of an autonomous vehicle that operate to control the autonomous vehicle. The operational messages can include operational data regarding the control or operation of the autonomous vehicle. The vehicle intention system can determine an intention of the autonomous vehicle based at least in part on the one or more operational messages. The vehicle intention system can output one or more intention signals that indicate the determined intention of the autonomous vehicle. For example, the vehicle intention system can publish intention messages that indicate the determined intention to one or more components or systems that consume the intention messages.

Autonomous vehicles featuring vehicle intention system

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.

Systems and methods for autonomous vehicle testing are provided. In one example embodiment, a computer implemented method includes obtaining, by a computing system including one or more computing devices, simulated perception data indicative of one or more simulated states of at least one simulated object within a surrounding environment of an autonomous vehicle. The computer-implemented method includes determining, by the computing system, a motion of the autonomous vehicle based at least in part on the simulated perception data. The computer-implemented method includes causing, by the computing system, the autonomous vehicle to travel in accordance with the determined motion of the autonomous vehicle through the surrounding environment of the autonomous vehicle.

Autonomous vehicle hybrid simulation testing

Systems and methods for controlling an autonomous vehicle to assist another autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining data representing a vehicle route of a first autonomous vehicle, wherein the first autonomous vehicle travels along the vehicle route from a first location to a second location. The method includes obtaining data representing an occlusion point that affects an operation of the first autonomous vehicle along the vehicle route. The method includes selecting a second autonomous vehicle, based at least in part on (i) the vehicle route and (ii) the occlusion point, to assist the first autonomous vehicle. The method includes deploying the second autonomous vehicle to assist the first autonomous vehicle to travel along the vehicle route.

Systems and methods for deploying an autonomous vehicle to oversee autonomous navigation

Growing community of inventors

Pittsburgh, PA, United States of America

Eric Michael Perko