| Issue |
E3S Web Conf.
Volume 233, 2021
2020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
|
|
|---|---|---|
| Article Number | 04019 | |
| Number of page(s) | 5 | |
| Section | MEA2020-Mechanical Engineering and Automation | |
| DOI | https://doi.org/10.1051/e3sconf/202123304019 | |
| Published online | 27 January 2021 | |
Tracking Control of Intelligent Vehicle Lane Change Based on RLMPC
1 China Automotive Technology Research Center Co.,Ltd, 30093 Tianjin, China
2 Automotive Data of China (Tianjin) Co.,Ltd, 30093 Tianjin, China
* Corresponding author: houquanshan@catarc.ac.cn
Autonomous lane changing, as a key module to realize high-level automatic driving, has important practical significance for improving the driving safety, comfort and commuting efficiency of vehicles. Traditional controllers have disadvantages such as weak scene adaptability and difficulty in balancing multi-objective optimization. In this paper, combined with the excellent self-learning ability of reinforcement learning, an interactive model predictive control algorithm is designed to realize the tracking control of the lane change trajectory. At the same time, two typical scenarios are verified by PreScan and Simulink, and the results show that the control algorithm can significantly improve the tracking accuracy and stability of the lane change trajectory.
© The Authors, published by EDP Sciences 2021
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