SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation
<|reference_start|>SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation: Verification and validation of autonomous driving (AD) systems and components is of increasing importance, as such technology increases in real-world prevalence. Safety-critical scenario generation is a key approach to robustify AD policies through closed-loop training. However, existing approaches for scenario generation rely on simplistic objectives, resulting in overly-aggressive or non-reactive adversarial behaviors. To generate diverse adversarial yet realistic scenarios, we propose SEAL, a scenario perturbation approach which leverages learned scoring functions and adversarial, human-like skills. SEAL-perturbed scenarios are more realistic than SOTA baselines, leading to improved ego task success across real-world, in-distribution, and out-of-distribution scenarios, of more than 20%. To facilitate future research, we release our code and tools: https://github.com/cmubig/SEAL<|reference_end|>
@article{stoler2024seal:,
    title={SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary
  Learning for Closed-Loop Scenario Generation},
    author={Benjamin Stoler, Ingrid Navarro, Jonathan Francis, Jean Oh},
    journal={arXiv preprint arXiv:2409.10320},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10320},
    primaryClass={cs.RO cs.AI cs.LG}
}