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arXiv 2105.10014cs.LGcs.AIcs.CVcs.RO

Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

ENSTA Paris(巴黎高等先进技术学院);Institut Polytechnique de Paris(巴黎理工学院);INRIA(法国国家信息与自动化研究所);Université Paris-Saclay(巴黎萨克雷大学);CEA(法国原子能和替代能源委员会);List(李斯特研究所)

Florence Carton, David Filliat, Jaonary Rabarisoa, Quoc Cuong Pham

英文摘要:

In recent years, we have witnessed increasingly high performance in the field of autonomous end-to-end driving. In particular, more and more research is being done on driving in urban environments, where the car has to follow high level commands to navigate. However, few evaluations are made on the ability of these agents to react in an unexpected situation. Specifically, no evaluations are conducted on the robustness of driving agents in the event of a bad high-level command. We propose here an evaluation method, namely a benchmark that allows to assess the robustness of an agent, and to appreciate its understanding of the environment through its ability to keep a safe behavior, regardless of the instruction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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