Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures
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 pre...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Robotics: Science and Systems Foundation
2022
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| Online Access: | https://hdl.handle.net/1721.1/145543 |