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About the Research
Mid-to-Mid Autonomous Driving based on Reinforcement Learning (RL)
Address safety-critical scenarios and long-tail distribution challenges beyond imitation learning (IL) capabilities
Integrate and convert real-world autonomous driving datasets (nuPlan, Waymo, etc.) into a unified simulation-ready format
Design reward functions tailored for effective RL training in diverse and rare scenarios

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