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Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

Abstract : In recent years, we have witnessed increasingly high performance in the field of autonomous end-toend 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.
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Contributor : David Filliat Connect in order to contact the contributor
Submitted on : Thursday, October 14, 2021 - 12:16:55 PM
Last modification on : Tuesday, November 2, 2021 - 11:11:26 AM


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  • HAL Id : hal-03377799, version 1


Florence Carton, David Filliat, Jaonary Rabarisoa, Quoc Pham. Evaluating Robustness over High Level Driving Instruction for Autonomous Driving. IV 2021 - 32nd IEEE Intelligent Vehicles Symposium, Jul 2021, Nagoya, Japan. ⟨hal-03377799⟩



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