Zoox, Inc.

Dhanushka Nirmevan Kularatne

Mark Jonathon McClelland

Marin Kobilarov

Subhasis Das

Joseph Funke

Zeng Wang

Janek Hudecek

Yuanyuan Chen

David Evan Zlotnik

Zhenqi Huang

Xianan Huang

Joshua Dean Egbert

Troy Donovan O′Neal

Techniques for accurately predicting vehicle state errors for avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model determine a representation of the vehicle usable in a scenario. The model may dynamically determine a size and/or a heading of the vehicle representation based on differences between a candidate trajectory and a current trajectory of the vehicle. The safety system can identify a potential collision between the vehicle and the object based at least in part on an overlap of the vehicle representation and an object representation.

Vehicle representation determination

Techniques for collision avoidance using an object contour are discussed. A trajectory associated with a vehicle may be received. Sensor data can be received from a sensor associated with the vehicle. A bounding contour may be determined and associated with an object represented in the sensor data. Based on the trajectory, a simulated position of the vehicle can be determined. Additionally, a predicted position of the bounding contour can be determined. Based on the simulated position of the vehicle and the predicted position of the bounding contour, a distance between the vehicle and the object may be determined. An action can be performed based on the distance between the vehicle and the object.

Collision avoidance using an object contour

The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

Unstructured vehicle path planner

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Menlo Park, CA, United States of America

Dhanushka Nirmevan Kularatne