Realistic Extreme Behavior Generation for Improved AV Testing
<|reference_start|>Realistic Extreme Behavior Generation for Improved AV Testing: This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collision scenarios adapted from real-world, collision-free data. Our framework generates counterfactual collisions with diverse crash properties, e.g., crash angle and velocity, between an adversary and a target vehicle by adding perturbations to the adversary's predicted trajectory from a learned AV behavior model. Our main contribution is to ground these adversarial perturbations in realistic behavior as defined through the lens of data-alignment in the behavior model's parameter space. Then, we cluster these synthetic counterfactuals to identify plausible and representative collision scenarios to form the basis of a test suite for downstream AV system evaluation. We demonstrate our framework using two state-of-the-art behavior prediction models as sources of realistic adversarial perturbations, and show that our scenario clustering evokes interpretable failure modes from a baseline AV policy under evaluation.<|reference_end|>
@article{dyro2024realistic,
    title={Realistic Extreme Behavior Generation for Improved AV Testing},
    author={Robert Dyro, Matthew Foutter, Ruolin Li, Luigi Di Lillo, Edward
  Schmerling, Xilin Zhou, Marco Pavone},
    journal={arXiv preprint arXiv:2409.10669},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10669},
    primaryClass={math.OC cs.RO}
}