@inproceedings{750e8816ee064a3d875c870c0f6cb08d,
title = "Research on Lane Change Intention Prediction Based on Fusion of Vehicle Forward Features",
abstract = "In mixed traffic environments with autonomous and traditional vehicles, perceiving the lane change intention of vehicles in advance is crucial to ensure traffic safety. Considering the current issue of lane change intention detection methods overemphasize the surrounding features of the host vehicle and have low accuracy, this paper proposes a lane change intention prediction algorithm that only fuses the vehicle forward features. This study first extract lane-changing trajectory data of vehicles that meet the definition of lane change from the NGSIM dataset based on changes in the lane marking. Then, the motion features and the forward traffic state features of the host vehicle are extracted from these lane-changing trajectory data to construct the dataset for predicting the vehicle{\textquoteright}s lane change intention. Finally, we use the LSTM_Self-Attention model to predict the lane-changing intention for different lead times. The results show that the LSTM_self-Attention model proposed in this study performs well for predicting vehicle{\textquoteright}s lane change intention. The prediction accuracy can reach 82% two seconds before the lane change and remains at 70% three seconds before the lane change, being of great significance for improving the safety of Autonomous driving.",
keywords = "Autonomous driving, Lane change intention prediction, LSTM_self-attention, NGSIM, Vehicle forward features",
author = "Jie Zhang and Wuhong Wang and Haodong Zhang and Haiqiu Tan and Dongxian Sun and Jian Shi and Yihao Si",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 13th International Conference on Green Intelligent Transportation Systems and Safety, GITSS 2022 ; Conference date: 16-09-2022 Through 18-09-2022",
year = "2024",
doi = "10.1007/978-981-97-3005-6_26",
language = "English",
isbn = "9789819730049",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "375--391",
editor = "Wuhong Wang and Guangquan Lu and Yihao Si",
booktitle = "Smart Transportation and Green Mobility Safety - Smart Transportation",
address = "Germany",
}