Vehicle lane change trajectory learning and prediction model considering vehicle interactions and driving styles in highway scene Online publication date: Thu, 16-Nov-2023
by Chenyu Song; Zhizhong Ding; Wanli Xu
International Journal of Sensor Networks (IJSNET), Vol. 43, No. 3, 2023
Abstract: Accurate lane change trajectory prediction is of great significance for improving driving safety in the highway scene. However, current methods ignore the fact that the driver's subjective manipulation fundamentally affects the trajectory. By analysing our real driving data on highways, we found that the vehicle interaction and the driver's driving style affected driver's operation heavily. Therefore, the innovation of this paper is to comprehensively consider the influence of vehicle interaction and driving style on the lane change trajectory to improve the accuracy of lane change trajectory prediction. The vehicle interaction and the driver's driving style are modelled jointly as a BiLSTM-based module and a cluster-based module to reduce prediction uncertainty. The HighD dataset is used to test the model, which shows that the mean absolute error of displacement prediction is reduced by nearly 24%, which achieves better results than some other existing models.
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