RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based
  Reinforcement Learning
Existing end-to-end autonomous driving (AD) algorithms typically follow the
Imitation Learning (IL) paradigm, which faces challenges such as causal
confusion and the open-loop gap. In this work, we establish a 3DGS-based
closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS
techniques, we construct a photorealistic digital replica of the real physical
world, enabling the AD policy to extensively explore the state space and learn
to handle out-of-distribution scenarios through large-scale trial and error. To
enhance safety, we design specialized rewards that guide the policy to
effectively respond to safety-critical events and understand real-world causal
relationships. For better alignment with human driving behavior, IL is
incorporated into RL training as a regularization term. We introduce a
closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS
environments. Compared to IL-based methods, RAD achieves stronger performance
in most closed-loop metrics, especially 3x lower collision rate. Abundant
closed-loop results are presented at https://hgao-cv.github.io/RAD.
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Hao Gao
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2025-02-19T09:00:48.944Z
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Hao Gao
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Shaoyu Chen
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Shaoyu Chen
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Bo Jiang
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Bencheng Liao
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Yiang Shi
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Xiaoyang Guo
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Yuechuan Pu
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Haoran Yin
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Xiangyu Li
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2025-02-20T16:05:51.144Z
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xiangyu
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xiangyuli
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Xinbang Zhang
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Ying Zhang
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Wenyu Liu
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2025-02-20T16:05:33.229Z
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LWY
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wenyuliu
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Qian Zhang
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Xinggang Wang
admin_assigned
2025-02-20T16:05:15.793Z
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Xinggang Wang
user
xinggangw
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based
  Reinforcement Learning
Existing end-to-end autonomous driving (AD) algorithms typically follow the
Imitation Learning (IL) paradigm, which faces challenges such as causal
confusion and the open-loop gap. In this work, we establish a 3DGS-based
closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS
techniques, we construct a photorealistic digital replica of the real physical
world, enabling the AD policy to extensively explore the state space and learn
to handle out-of-distribution scenarios through large-scale trial and error. To
enhance safety, we design specialized rewards that guide the policy to
effectively respond to safety-critical events and understand real-world causal
relationships. For better alignment with human driving behavior, IL is
incorporated into RL training as a regularization term. We introduce a
closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS
environments. Compared to IL-based methods, RAD achieves stronger performance
in most closed-loop metrics, especially 3x lower collision rate. Abundant
closed-loop results are presented at https://hgao-cv.github.io/RAD.
3DGS-based closed-loop reinforcement learning for autonomous driving integrates photorealistic simulation and specialized rewards, reducing collision rates compared to imitation learning.
Reinforcement Learning Imitation Learning 3DGS photorealistic digital replica state space out-of-distribution scenarios safety-critical events causal relationships closed-loop evaluation benchmark autonomous driving