Citation

Zhang Y, Zou Y, Tang J, Liang J. Transportmetrica B: Transp. Dyn. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Hong Kong Society for Transportation Studies, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/21680566.2021.1950072

PMID

unavailable

Abstract

Lane-changing is an important driving behaviour and unreasonable lane changes can potentially result in traffic accidents. Currently, the lane-changing data are often recorded with high resolution, which are not appropriate for some common deep learning approaches. To capture the stochastic time series of high-resolution lane-changing behaviour, this study introduces a temporal convolutional network (TCN) to predict the long-term lane-changing trajectory and behaviour. The lane-changing dataset was collected by the driving simulator at the frequency of 60 Hz. Prediction results show that the TCN can accurately predict the long-term lane-changing trajectory and driving behaviour with shorter computational time compared with two benchmark models including the convolutional neural network (CNN) and long short-term memory neural network (LSTM). The advantages of the TCN are rapid response and accurate long-term prediction, which are important for lane-changing assistance in the advanced driver assistance system.

Language: en

Keywords

driving behaviour; Lane changes; temporal convolution network; time series; trajectory prediction