  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.
