Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures

27 May 2020 Allen Wang Xin Huang Ashkan Jasour Brian Williams

This paper presents fast non-sampling based methods to assess the risk of trajectories for autonomous vehicles when probabilistic predictions of other agents' futures are generated by deep neural networks (DNNs). The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models for predictions of both agent positions and controls... (read more)

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