  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, we argue that
adversarial evaluation fundamentally requires a process that seeks to defeat a
specific protection. Hence, we propose that evaluations be based on estimating
the difficulty for an adversary to determine conditions that effectively induce
unsafe behavior. This type of inference requires precise statements about
threats, protections, and aspects of planning decisions to be guarded. We
demonstrate our approach by evaluating protections for planners relying on
camera-based object detectors.
