Evaluating Automated Driving Planner Robustness against Adversarial Influence. (arXiv:2205.14697v1 [cs.CR])

Evaluating the robustness of automated driving planners is a critical and
challenging task. Although methodologies to evaluate vehicles are well
established, they do not yet account for a reality in which vehicles with
autonomous components share the road with adversarial agents. Our approach,
based on probabilistic trust models, aims to help researchers assess the
robustness of protections for machine learning-enabled planners against
adversarial influence. In contrast with established practices that evaluate
safety using the same evaluation dataset for all vehicles, …

adversarial influence