Research Article
Vehicle Trajectory Prediction Model Based on Fusion Neural Network
@INPROCEEDINGS{10.1007/978-3-031-34790-0_24, author={Xuemei Mou and Xiang Yu and Binbin Wang and Ziyi Wang and Fugui Deng}, title={Vehicle Trajectory Prediction Model Based on Fusion Neural Network}, proceedings={Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings}, proceedings_a={CHINACOM}, year={2023}, month={6}, keywords={Intelligent traffic Trajectory prediction Deep learning Spatial-temporal relationships}, doi={10.1007/978-3-031-34790-0_24} }- Xuemei Mou
Xiang Yu
Binbin Wang
Ziyi Wang
Fugui Deng
Year: 2023
Vehicle Trajectory Prediction Model Based on Fusion Neural Network
CHINACOM
Springer
DOI: 10.1007/978-3-031-34790-0_24
Abstract
To address the problem of the lack of interpretability of vehicle trajectory prediction models based on deep learning, this paper proposes a Fusion Neural network with the Spatio-Temporal Attention (STA-FNet) model. The model outputs a predictive distribution of future vehicle trajectories based on different vehicle trajectories and traffic environment factors, with an in-depth analysis of the Spatio-temporal attention weights learned from various urban road traffic scenarios. In this paper, the proposed model is evaluated using the publicly available NGSIM dataset, and the experimental results show that the model not only explains the influence of historical trajectories and road traffic environment on the target vehicle trajectories but also obtains better prediction results in complex traffic environments.
