Data-Driven Trajectory Tracking Control Algorithm Design for Fast Migration to Different Autonomous Vehicles - Cao, Yueming; Ke, Zehong; Li, Shangyi; Zhao, Shibin; Wang, Jianqiang; Xu, Shaobing (IEEE Intelligent Vehicles Symposium, 2024)
Deploying a certain trajectory tracking control algorithm to a different autonomous vehicle is time-consuming as most control parameters need to be fine-tuned, which necessitates a series of experiments and computations to ascertain the appropriate parameters considering factors such as tire stiffness, inertia, wheelbase, and speed levels. This paper introduces a data-driven method for automatic optimization of the parameters for fast migration. We leverage preview control as the fundamental algorithm whose effectiveness has been proven on an automated Lincoln MKZ in our previous studies and aim to deploy it to other autonomous cars more efficiently. To learn the control parameters, naturalistic driving data is collected, but one challenge is that the data lacks tracking error information. The proposed strategy decouples the algorithm into feedforward control and feedback control, then learns the two control parts separately with well-designed experiments. Multiple schemes of neural networks are devised to identify feedforward and feedback gains, then the optimal scheme is selected to form the preview control for the given target vehicle. The deployment and validation take place on both simulation and a 4×4 off-road platform. Tests show that the proposed pipeline can learn the control gains for a certain speed range with only around 0.5 hours of driving data, which accelerates the control deployment compared to empirical human-based fine tuning. Compared to the pure pursuit control, the control identified by the proposed method demonstrates outstanding performance in terms of accuracy and stability at the specific speed range.