TR2019-074

Reinforcement Learning for Vision-Based Lateral Control of a Self-Driving Car


    •  Huang, M., Zhao, M., Parikh, P., Wang, Y., Ozbay, K., Jiang, Z.-P., "Reinforcement Learning for Vision-Based Lateral Control of a Self-Driving Car", IEEE International Conference on Control and Automation (ICCA), DOI: 10.1109/JAS.2019.19117678, July 2019, pp. 1126-1131.
      BibTeX Download PDF
      • @inproceedings{Huang2019jul,
      • author = {Huang, Mengzhe and Zhao, Mingyu and Parikh, Parthiv and Wang, Yebin and Ozbay, Kaan and Jiang, Zhong-Ping},
      • title = {Reinforcement Learning for Vision-Based Lateral Control of a Self-Driving Car},
      • booktitle = {IEEE International Conference on Control and Automation (ICCA)},
      • year = 2019,
      • pages = {1126--1131},
      • month = jul,
      • doi = {10.1109/JAS.2019.19117678},
      • url = {https://www.merl.com/publications/TR2019-074}
      • }
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  • Research Areas:

    Computer Vision, Control, Machine Learning


Lateral control design is one of the fundamental components for self-driving cars. In this paper, we propose a learning-based control strategy that enables a mobile car equipped with a camera to perfectly perform lane keeping in a road on the ground. Using the method of adaptive dynamic programming, the proposed control algorithm exploits the structural knowledge of the car kinematics as well as the collected data (images) about the lane information. An adaptive optimal lateral controller is obtained through a data-driven learning algorithm. The effectiveness of the proposed method is demonstrated by theoretical stability proofs and experimental evaluations.