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The Hong Kong Polytechnic University, Tongji University
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AWM enables autonomous vehicles to effectively learn from adversarial scenarios, significantly enhancing their robustness in rare and critical traffic conditions.
MobEvolve outperforms traditional methods by evolving its logic through targeted updates, achieving unprecedented fidelity and interpretability in human mobility generation.
By framing adversarial training as a zero-sum Markov game, ADV-0 finds more diverse safety-critical failures in autonomous driving systems, leading to significantly improved generalization against unseen long-tail risks.
