Continual Reinforcement Learning Framework for Scalable Collision Avoidance and Mitigation System With Packing Strategy

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这篇工作首先强调：Collision Avoidance and Mitigation System (CAMS) in autonomous driving systems is crucial for ensuring safety by formulating strategies to address various collisions and planning the trajectory accordingly.

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方法线上更接近 强化学习。Although recent learning-based motion planning methods for CAMS have shown promising results for specific collision scenarios, the question of how to continually scale up their knowledge across different driving environments has not yet been thoroughly investigated.