Uatc, LLC

Uber Technologies, Inc.

Aurora Operations, Inc.

Alexander David Styler

Galen Clark Haynes

Tian Lan

Anthony Joseph Stentz

Nemanja Djuric

Jeff Schneider

Micol Marchetti-Bowick

Vladan Radosavljevic

Tzu-Kuo Huang

Tsung-Han Lin

Neil Traft

Alexander Rashid Ansari

Ian Dewancker

Huahua Wang

Systems and methods for determining object motion and controlling autonomous vehicles are provided. In one example embodiment, a computing system includes processor(s) and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the processor(s) cause the computing system to perform operations. The operations include obtaining data associated with a first object and one or more second objects within a surrounding environment of an autonomous vehicle. The operations include determining an interaction between the first object and the one or more second objects based at least in part on the data. The operations include determining one or more predicted trajectories of the first object within the surrounding environment based at least in part on the interaction between the first object and the one or more second objects. The operations include outputting data indicative of the one or more predicted trajectories of the first object.

Object interaction prediction systems and methods for autonomous vehicles

The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.

Machine learning for predicting locations of objects perceived by autonomous vehicles

Systems and methods for determining object motion and controlling autonomous vehicles are provided. In one example embodiment, a computing system can be configured to perform operations. The operations can include obtaining data indicative of state(s) of a first object and a second object within a surrounding environment of an autonomous vehicle. The operations can include determining a first predicted motion trajectory of the first object based at least in part on the state data. The operations can include determining a second predicted motion trajectory of the second object based at least in part on the state data and the first predicted motion trajectory of the first object. The operations can include determining a motion plan for the autonomous vehicle based at least in part on the second predicted motion trajectory of the second object and initiating a motion control in accordance with at least a portion of the motion plan.

Object motion prediction and vehicle control systems and methods for autonomous vehicles

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Pittsburgh, PA, United States of America

Alexander David Styler