TY  - JOUR
PY  - 2021//
TI  - Long-term prediction for high-resolution lane-changing data using temporal convolution network
JO  - Transportmetrica B: transport dynamics
A1  - Zhang, Yue
A1  - Zou, Yajie
A1  - Tang, Jinjun
A1  - Liang, Jian
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 2168-0566
UR  - http://dx.doi.org/10.1080/21680566.2021.1950072
ID  - ref1
ER  -