Aurora Operations, Inc.

Skanda Shridhar

Galen Clark Haynes

Yuhang Ma

Zhengdi Shen

Neil Traft

Tara Lynn Stentz

Systems and methods for generating performance metrics for autonomous vehicle systems are provided. The performance metrics include two complementary metrics that evaluate a machine-learning object prediction model relative to a number of potential trajectories of an autonomous vehicle. The performance metrics include an avoidance metric that quantifies a probability that a region occupied by a real-world or simulated object is reached by the autonomous vehicle, the region is not blocked by another object, and the region is not blocked by a prediction output by the machine-learning object prediction model. The performance metrics also include an availability metric that quantifies a probability that a simulated or real-world object is not located within a region, the region is not blocked by another simulated or real-world object, and the autonomous vehicle is unnecessarily blocked by the prediction output before the autonomous vehicle reaches the particular footprint.

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Pittsburgh, PA, United States of America

Skanda Shridhar