Evaluating Robustness over High Level Driving Instruction for Autonomous Driving
On the map: End-to-end autonomous driving → Adversarial scenarios and safety testing
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving. Builds on 23 of its 34 references that are in the map. First posted on arXiv 20 May 2021.
Builds on
23 of 34 references.
References · 23
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11 more
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Citing papers per year: 2024: 1.
1 paper.
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