期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 69, 期 11, 页码 12482-12496出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2020.3020335
关键词
Vehicles; Acceleration; Automotive engineering; Adaptive systems; Cruise control; Predictive control; Machine learning; Personalized adaptive cruise control; driving style recognition; model predictive control; machine learning
资金
- National Key Research and Development Program [2016YFB0100904]
- National Nature Science Foundation of China [61703178, 61790564, 61703176]
- China Automobile Industry and Development Joint Fund [U1664257, U1864201]
- Jilin Provincial Science Foundation of China [20180520200JH]
- Joint Project of Jilin University [SXGJSF2017-2-1-1]
This paper proposes a personalized adaptive cruise control (ACC) system based on driving style recognition and model predictive control (MPC) to meet different driving styles while ensuring car-following, comfort and fuel-economy performances. To obtain the controller parameters corresponding to different driving styles, a set of real vehicle experiments are conducted to collect driving data of 66 randomly recruited drivers, then the experimental data is clustered through unsupervised machine learning method. On the basis of it, a driving style classifier is designed by supervised machine learning method, which can be used to recognize the driving style of drivers online. Then, the control problem of the personalized ACC system is described as a multi-objective optimization problem which is solved by MPC method. The simulation results show that the proposed personalized ACC system can meet the requirements of different driving styles and guarantee various performances.
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