@article{ref1,
title="Long-term prediction for high-resolution lane-changing data using temporal convolution network",
journal="Transportmetrica B: transport dynamics",
year="2021",
author="Zhang, Yue and Zou, Yajie and Tang, Jinjun and Liang, Jian",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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.<p /> <p>Language: en</p>",
language="en",
issn="2168-0566",
doi="10.1080/21680566.2021.1950072",
url="http://dx.doi.org/10.1080/21680566.2021.1950072"
}