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M2M RL Autonomous Driving

-Mid-to-Mid Planning
-Imitation Learning
-Reinforcement Learning

Year

2025

Workspace

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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E  kichunjo@hanyang.ac.kr

T  02-2220-0449 

A  Room #506, Chung Mong-Koo Automotive Research Center,

     222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea

  • 유튜브 - 흰색 원
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