Abstract: In order to better realize personalized driving, this paper proposes a self-learning lane-change trajectory planning system integrating driver characteristics identification. Firstly, this paper introduces the driver characteristic coefficient J_c and the driver response and operation time t_d into the Gaussian distribution to establish a personalized lane-change trajectory planning model, and matches the actual trajectory with the fitting trajectory through the DTW algorithm. After that, AP clustering is carried out based on the collected driver lane-change trajectories and the general values of J_c and t_d are calibrated offline. At the same time, the labels of 30 drivers are obtained, and their driving characteristics are divided into comfort, normal and sport type. Then, the free driving data are extracted for characteristic working conditions, and an online identification model of driver characteristics is built based on long-term and short-term memory network (LSTM). Finally, 15 drivers are selected for real-car verification. The system extracts characteristic conditions in real time and then adjusted J_c and t_d online based on the recognition results, and continuously updates the fitting trajectory. After the experiment, the squared Euclidean distance between the actual trajectory and the fitting trajectory of the 14 drivers is less than 1, with the fitting accuracy reaching 93.3%. Therefore, the system can reproduce the trajectory of real lane-change well.

Key words: driver, identification of characteristics, long-term and short-term memory network, self-learning, lane-change trajectory planning