2021 25th International Conference on Methods and Models in Automation and Robotics (MMAR) 2021
DOI: 10.1109/mmar49549.2021.9528492
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Adversarial Trajectories Generation for Automotive Applications

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“…While the errors introduced in the SM environment in this study were severe enough to expose the hazards, realistic sensor errors observed in good weather and lighting conditions may not be sufficient to trigger safety-critical errors in the evaluation with real-world data. Possible ways to alleviate this issue include testing in various SM environments, or the use of automatically-generated adversarial test scenarios to ensure good coverage of edge cases [25].…”
Section: Discussionmentioning
confidence: 99%
“…While the errors introduced in the SM environment in this study were severe enough to expose the hazards, realistic sensor errors observed in good weather and lighting conditions may not be sufficient to trigger safety-critical errors in the evaluation with real-world data. Possible ways to alleviate this issue include testing in various SM environments, or the use of automatically-generated adversarial test scenarios to ensure good coverage of edge cases [25].…”
Section: Discussionmentioning
confidence: 99%