Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification

arXiv:2404.18573v1 Announce Type: new
Abstract: The automated real-time recognition of unexpected situations plays a crucial role in the safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This paper evaluates different Bayesian uncertainty quantification methods from the deep learning domain for the anticipatory testing of safety-critical misbehaviours during system-level simulation-based testing. Specifically, we compute uncertainty scores as the vehicle executes, following the intuition that high uncertainty scores are indicative of unsupported runtime conditions that can be used to distinguish …

abstract arxiv automated autonomous autonomous driving autonomous driving systems autonomous vehicles bayesian cs.lg cs.se deep learning domain driving paper quantification real-time recognition role safety safety-critical systems testing type uncertainty vehicles