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Intelligent mobility systems are increasingly making use of AI for various functions, including navigation, sign recognition, road tracking, and obstacle detection. To achieve certification up to SAE Level 3 and more in the future, manufacturers must prove that their vehicles maintain adequate safety within their operational design domain through rigorous testing in diverse scenarios. Sensor simulation tools including degraded weather conditions (physical, numerical or hybrid) must be employed. In this study as part of the PRISSMA project, a proof of concept is proposed to characterize and evaluate the protocols and four different kind of simulation tools that enable AI algorithm certification under degraded weather conditions.

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Assessment of Uncertainty and Variability in Simulation Tools Under Foggy Conditions

  • 会议论文

  • 发表日期：

    2026年

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  • Pierre Duthon,
  • Mohamed Boudali,
  • Amine Ben-Daoued,
  • Rémi Regnier,
  • Charlotte Segonne,
  • Frédéric Bernardin

摘要

Intelligent mobility systems are increasingly making use of AI for various functions, including navigation, sign recognition, road tracking, and obstacle detection. To achieve certification up to SAE Level 3 and more in the future, manufacturers must prove that their vehicles maintain adequate safety within their operational design domain through rigorous testing in diverse scenarios. Sensor simulation tools including degraded weather conditions (physical, numerical or hybrid) must be employed. In this study as part of the PRISSMA project, a proof of concept is proposed to characterize and evaluate the protocols and four different kind of simulation tools that enable AI algorithm certification under degraded weather conditions.