Uatc, LLC

Aurora Operations, Inc.

Samuel Philip Marden

David McAllister Bradley

Michael Lee Phillips

Eric Michael Perko

Colin Jeffrey Green

Eric Lloyd Wilkinson

Kalin Vasilev Gochev

Anthony Joseph Stentz

Wei Liu

Don Burnette

Somchaya Liemhetcharat

Harishma Dayanidhi

Utku Eren

Chenggang Liu

Moslem Kazemi

Daoyuan Jia

Zakary Warren Littlefield

Aum Jadhav

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

Systems and methods for vehicle motion planning based on uncertainty are provided. A method can include obtaining scene data descriptive of one or more objects within a surrounding environment of the autonomous vehicle. The method can include determining one or more subproblems based at least in part on the scene data. In some implementation, each of the one or more subproblems can correspond to at least one object within the surrounding environment of the autonomous vehicle. The method can include generating one or more branching policies based at least in part on the one or more subproblems. In some implementations, each of the one or more branching policies can include scene data associated with the autonomous vehicle and one or more objects within the surrounding environment of the autonomous vehicle. The method can include determining one or more costs associated each of the one or more branching policies. The method can include selecting a motion plan based at least in part on the one or more costs associated with each of the one or more branching policies. The method can include providing the motion plan for use in controlling a motion of the autonomous vehicle.

Systems and methods for vehicle motion planning based on uncertainty

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

Systems and methods are directed to lane change control for an autonomous vehicle. In one example, a computer-implemented method for determining whether to abort a lane change in an autonomous vehicle includes initiating, by a computing system comprising one or more computing devices, a lane change procedure for an autonomous vehicle. The method further includes obtaining, by the computing system, data indicative of one or more changed objects relative to the autonomous vehicle. The method further includes determining, by the computing system, that the lane change procedure cannot be completed by the autonomous vehicle based at least in part on the data indicative of one or more changed objects. The method further includes in response to determining that the lane change cannot be completed by the autonomous vehicle, generating, by the computing system, a motion plan that controls the autonomous vehicle to abort the lane change procedure.

Systems and methods for autonomous vehicle lane change control

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Pittsburgh, PA, United States of America

Samuel Philip Marden