ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving

ADV-0 is a new closed-loop adversarial training framework for autonomous driving that uses min-max optimization to improve robustness against rare but safety-critical scenarios. The system treats the interaction between driving policy and adversarial agents as a zero-sum game, converging to Nash Equilibrium while maximizing real-world performance bounds.