• Corpus ID: 239616109

ModEL: A Modularized End-to-end Reinforcement Learning Framework for Autonomous Driving

@article{Wang2021ModELAM,
  title={ModEL: A Modularized End-to-end Reinforcement Learning Framework for Autonomous Driving},
  author={Guan Wang and Haoyi Niu and Desheng Zhu and Jianming Hu and Xianyuan Zhan and Guyue Zhou},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11573}
}
  • Guan Wang, Haoyi Niu, +3 authors Guyue Zhou
  • Published 22 October 2021
  • Computer Science
  • ArXiv
Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability, whereas the end-to-end paradigm has demonstrated considerable simplicity and learnability along with the rise of deep learning. We introduce a new modularized end-to-end reinforcement learning framework (ModEL) for autonomous driving, which combines the merits of both previous approaches. The autonomous driving… 

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