RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909859311517696 |
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| author | Gao, Hao Chen, Shaoyu Jiang, Bo Liao, Bencheng Shi, Yiang Guo, Xiaoyang Pu, Yuechuan Yin, Haoran Li, Xiangyu Zhang, Xinbang Zhang, Ying Liu, Wenyu Zhang, Qian Wang, Xinggang |
| author_facet | Gao, Hao Chen, Shaoyu Jiang, Bo Liao, Bencheng Shi, Yiang Guo, Xiaoyang Pu, Yuechuan Yin, Haoran Li, Xiangyu Zhang, Xinbang Zhang, Ying Liu, Wenyu Zhang, Qian Wang, Xinggang |
| contents | Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and an open-loop gap. In this work, we propose RAD, a 3DGS-based closed-loop Reinforcement Learning (RL) framework for end-to-end Autonomous Driving. 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 to guide the policy in effectively responding to safety-critical events and understanding real-world causal relationships. To better align with human driving behavior, we incorporate IL 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, particularly exhibiting a 3x lower collision rate. Abundant closed-loop results are presented in the supplementary material. Code is available at https://github.com/hustvl/RAD for facilitating future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning Gao, Hao Chen, Shaoyu Jiang, Bo Liao, Bencheng Shi, Yiang Guo, Xiaoyang Pu, Yuechuan Yin, Haoran Li, Xiangyu Zhang, Xinbang Zhang, Ying Liu, Wenyu Zhang, Qian Wang, Xinggang Computer Vision and Pattern Recognition Robotics Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and an open-loop gap. In this work, we propose RAD, a 3DGS-based closed-loop Reinforcement Learning (RL) framework for end-to-end Autonomous Driving. 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 to guide the policy in effectively responding to safety-critical events and understanding real-world causal relationships. To better align with human driving behavior, we incorporate IL 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, particularly exhibiting a 3x lower collision rate. Abundant closed-loop results are presented in the supplementary material. Code is available at https://github.com/hustvl/RAD for facilitating future research. |
| title | RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2502.13144 |