Uatc, LLC

Uber Technologies, Inc.

Aurora Operations, Inc.

Eric Lloyd Wilkinson

David McAllister Bradley

Michael Lee Phillips

Colin Jeffrey Green

Samuel Philip Marden

Anthony Joseph Stentz

Eric Michael Perko

Wei Liu

J Andrew Bagnell

Kalin Vasilev Gochev

Don Burnette

Somchaya Liemhetcharat

Jacob Panikulam

Harishma Dayanidhi

Utku Eren

Aum Jadhav

Zakary Warren Littlefield

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

The present disclosure provides systems and methods that control the motion of an autonomous vehicle by rewarding or otherwise encouraging progress toward a goal, rather than simply rewarding distance travelled. In particular, the systems and methods of the present disclosure can project a candidate motion plan that describes a proposed motion path for the autonomous vehicle onto a nominal pathway to determine a projected distance associated with the candidate motion plan. The systems and methods of the present disclosure can use the projected distance to evaluate a reward function that provides a reward that is positively correlated to the magnitude of the projected distance. The motion of the vehicle can be controlled based on the reward value provided by the reward function. For example, the candidate motion plan can be selected for implementation or revised based at least in part on the determined reward value.

Systems and methods to control autonomous vehicle motion

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

Generally, the present disclosure is directed to systems and methods that include or otherwise leverage a decision point engine as part of determining a motion plan for an autonomous vehicle. In particular, a motion planning system that includes a decision point engine can be configured to obtain object data associated with one or more objects identified near one or more travel paths of an autonomous vehicle. The system can determine a stopping profile based at least in part on the object data, wherein the stopping profile identifies a set of candidate states for the autonomous vehicle that preserves an ability of the autonomous vehicle to stop before the interaction point. The system can determine a decision point corresponding to a selected state from the set of candidate states identified by the stopping profile, and determine a trajectory for the autonomous vehicle based at least in part on the decision point.

Motion planning system of an autonomous vehicle

The present disclosure provides systems and methods that control the motion of an autonomous vehicle by rewarding or otherwise encouraging progress toward a goal, rather than simply rewarding distance traveled. In particular, the systems and methods of the present disclosure can project a candidate motion plan that describes a proposed motion path for the autonomous vehicle onto a nominal pathway to determine a projected distance associated with the candidate motion plan. The systems and methods of the present disclosure can use the projected distance to evaluate a reward function that provides a reward that is positively correlated to the magnitude of the projected distance. The motion of the vehicle can be controlled based on the reward value provided by the reward function. For example, the candidate motion plan can be selected for implementation or revised based at least in part on the determined reward value.

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Pittsburgh, PA, United States of America

Eric Lloyd Wilkinson