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.

变道是一种重要的驾驶行为，不合理的变道可能导致交通事故。目前，车道变换数据通常以高分辨率记录，这不适用于一些常见的深度学习方法。为了捕捉高分辨率变道行为的随机时间序列，本研究引入了时间卷积网络（TCN）来预测长期变道轨迹和行为。车道变换数据集由驾驶模拟器以 60 Hz 的频率收集。预测结果表明，与卷积神经网络（CNN）和长短期记忆神经网络（LSTM）两种基准模型相比，TCN能够以更短的计算时间准确预测长期变道轨迹和驾驶行为。