Zoox, Inc.

Ruikun Yu

Marin Kobilarov

Joseph Funke

Ravi Verma Gogna

Gowtham Garimella

Jefferson Bradfield Packer

Steven Cheng Qian

Jacob Patrick Thalman

Caili Li

Andreas Pasternak

Yiteng Zhang

Techniques for providing remote assistance to a vehicle are discussed. The techniques include receiving, from a vehicle, an indication of an event and displaying, on a display and to a user, a portion of an environment including the vehicle. The techniques further determine a valid region in the portion of the environment associated with a location at which the vehicle is capable of navigating. The techniques also display, on the display a footprint of the vehicle, where the footprint is associated with a position and orientation. The techniques further include transmitting the position and orientation of the footprint to the vehicle, which causes the vehicle to traverse in the environment to the position and orientation.

Remote assistance for vehicles

A reward determined as part of a machine learning technique, such as reinforcement learning, may be used to control an adversarial agent in a simulation such that a component for controlling motion of the adversarial agent is trained to reduce the reward. Training the adversarial agent component may be subject to one or more constraints and/or may be balanced against one or more additional goals. Additionally or alternatively, the reward may be used to alter scenario data so that the scenario data reduces the reward, allowing the discovery of difficult scenarios and/or prospective events.

Adversarial agent controls generation and problematic scenario forecasting

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San Jose, CA, United States of America

Ruikun Yu