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Nowadays, with more and more attention being paid to the characteristics and experience of drivers, a large number of driver classification algorithms have emerged. However, these methods basically cannot be adjusted independently to each driver. Therefore, this paper proposes a self-learning lane change motion planning system considering the driver’s personality. Firstly, the method of driver data acquisition and processing is determined to obtain and extract the lane change data. Then, the planning system built in this paper is explained from two aspects: lane change trigger and lane change trajectory. According to the artificial potential field theory, an obstacle driving risk field is established to evaluate the acceptance of environmental risks of different drivers, and to achieve personalized lane change triggers through online statistics. At the same time, the safety of lane change is ensured by establishing the safety distance model of the target lane. On the other hand, the driver characteristic coefficient Jc and the driver reaction and operation time td are introduced into the traditional Gaussian-distributed model to establish a personalized lane change trajectory planning model, in which the parameters are obtained from offline and online learning. Offline learning is based on DTW for trajectory matching, and uses AP clustering to obtain the generalized parameters; Online learning uses LSTM to achieve personalized updates. Finally, this paper selected 15 drivers for verification, and the results show that the motion planning system can well reproduce the lane change behavior of the driver.

This study presents a biosignal based driving experience analysis of an actual-vehicle experiment. A total of 10 subjects were enrolled during the experimental study. Based on their driving mileages per year, subjects were divided into novice and skilled ones at first, and then electromyography (EMG) signals of upper trapezius and sternocleidomastoid muscles were acquired continuously to evaluate a subject response to dynamic motions of the vehicle during the curve, with a driving speed of 30, 40 and 50 km/h respectively. Meanwhile, an EMG evaluation index of a normalized root mean square (RMS) was proposed to reflect the variance of EMG signals. From the experimental results, the RMS based evaluation of right upper trapezius muscle were significantly different between novice and skilled drivers, while driving with a higher speed on curve road. In addition, the RMS based evaluation of right sternocleidomastoid muscle were significantly different between novice and skilled drivers, while driving with a higher or a lower speed on curved road. It indicate that the skilled driver may have a better driving experience than that of novice ones for most curve driving conditions.