@inproceedings{5622b9aaedd24b3cbacaf9571b0df903,
title = "Multi-scenario Learning MPC for Automated Driving in Unknown and Changing Environments",
abstract = "System dynamics identification significantly impacts trajectory tracking performance for autonomous driving in a dynamic environment. In this paper, a multi-scenario learning model predictive control (MPC) optimization strategy is proposed to reduce model complexity and improve system generalization and robustness. First, the Gaussian process is simplified to reduce the complexity of the system's residual model while ensuring the optimization problem's convexity. Then, a meta-learning based multi-scenario model is proposed through online adjusting weight factors to identify the dynamic characteristics when the vehicle drives in a new scenario. Finally, the developed learning model is integrated into a stochastic MPC framework for robust optimization by considering environmental changes and parameter uncertainties. Simulation results show the efficient performance of our proposed method in terms of model prediction accuracy and trajectory tracking.",
keywords = "gaussian process, meta-learning, stochastic model predictive control, trajectory tracking",
author = "Yu Yue and Zhenpo Wang and Jianhong Liu and Guoqaing Li",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 21st IEEE International Conference on Industrial Informatics, INDIN 2023 ; Conference date: 17-07-2023 Through 20-07-2023",
year = "2023",
doi = "10.1109/INDIN51400.2023.10218123",
language = "English",
series = "IEEE International Conference on Industrial Informatics (INDIN)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Helene Dorksen and Stefano Scanzio and Jurgen Jasperneite and Lukasz Wisniewski and Man, {Kim Fung} and Thilo Sauter and Lucia Seno and Henning Trsek and Valeriy Vyatkin",
booktitle = "2023 IEEE 21st International Conference on Industrial Informatics, INDIN 2023",
address = "United States",
}