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ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events

Autonomous vehicles are advanced driving systems that are well known to be vulnerable to various adversarial attacks, compromising vehicle safety and posing a risk to other road users. Rather than actively training complex adversaries by interacting with the environment, there is a need to first intelligently find and reduce the search space to only those states where autonomous vehicles are found to be less confident. In this paper, we propose a black-box testing framework ReMAV that uses offline trajectories first to analyze the existing behavior of autonomous vehicles and determine appropriate thresholds to find the probability of failure events. To this end, we introduce a three-step methodology which i) uses offline state action pairs of any autonomous vehicle under test, ii) builds an abstract behavior representation using our designed reward modeling technique to analyze states with uncertain driving decisions, and iii) uses a disturbance model for minimal perturbation attacks where the driving decisions are less confident. Our reward modeling technique helps in creating a behavior representation that allows us to highlight regions of likely uncertain behavior even when the standard autonomous vehicle performs well. We perform our experiments in a high-fidelity urban driving environment using three different driving scenarios containing single- and multi-agent interactions. Our experiment shows an increase in 35, 23, 48, and 50% in the occurrences of vehicle collision, road object collision, pedestrian collision, and offroad steering events, respectively by the autonomous vehicle under test, demonstrating a significant increase in failure events. We compare ReMAV with two baselines and show that ReMAV demonstrates significantly better effectiveness in generating failure events compared to the baselines in all evaluation metrics.

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