Tusimple, Inc.

Long Sha

Panqu Wang

Xiangchen Zhao

Rundong Ge

Fangjun Zhang

Yizhe Zhao

Junliang Zhang

A unified framework for detecting perception anomalies in autonomous driving systems is described. The perception anomaly detection framework takes an input image from a camera in or on a vehicle and identifies anomalies as belonging to one of three categories. Lens anomalies are associated with poor sensor conditions, such as water, dirt, or overexposure. Environment anomalies are associated with unfamiliar changes to an environment. Finally, object anomalies are associated with unknown objects. After perception anomalies are detected, the results are sent downstream to cause a behavior change of the vehicle.

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San Diego, CA, United States of America

Long Sha