A Vehicle Lane-changing Trajectory Prediction Model Based on Temporal Convolutional Networks and Attention Mechanism

被引：0
|
作者：
Yang D. [1 ,2 ]
Liu J. [1 ]
Zheng B. [1 ]
Sun F. [1 ]
机构：
[1] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu
[2] Department of Civil and Environmental Engineering, University of Wisconsin, Madison, 53706, WI
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2024年 / 24卷 / 02期
基金：
中国国家自然科学基金;
关键词：
TCN-Attention; traffic engineering; vehicle lane-changing; vehicle trajectory prediction;
D O I：
10.16097/j.cnki.1009-6744.2024.02.012
中图分类号：
学科分类号：
摘要：
An accurate vehicle trajectory prediction model can provide self-driving vehicles with precise information about the motion states of surrounding vehicles in mixed traffic flow environments, allowing it to assess the possibility of conflicts with neighboring vehicles in the short term. This paper proposes a vehicle lane-changing trajectory prediction model based on Temporal Convolutional Networks with Attention Mechanism (TCN-Attention) to improve the accuracy of vehicle lane-changing trajectory prediction. This model uses Temporal Convolutional Networks as the current input's feature extractor and utilizes a temporal and spatial attention mechanism to establish dynamic correlations between different time steps and spatial positions. Specifically, the combination of temporal and spatial attention mechanisms helps the model extract essential semantic features in both the temporal and spatial dimensions before and after lane-changing, enabling it to more accurately capture the dynamic spatiotemporal relationships between vehicles. This enables precise predictions of lane-changing trajectories on highways. Different from the traditional only using a trajectory features as input, our method achieves the multi-dimensional expansion and fusion of the input features, and further improves the accuracy of the trajectory prediction. In addition, this paper proposes a new method to define the start and end time of lane-changing in the dataset more accurately. Experiments show that the proposed model can predict the trajectory of the lane-changing with high accuracy, and the overall effect is better than other deep learning models. Compared with the Convolution Long Short- Term Memory(ConvLSTM), the Mean Absolute Error(EMAE ) of TCN-Attention is reduced by 69.8%, the Root Mean Square Error(ERMSE ) is reduced by 49.15% and the Mean Absolute Percentage Error(EMAPE ) is reduced by 14.24%. © 2024 Science Press. All rights reserved.
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页码：114 / 126
页数：12
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