重庆邮电大学学报（自然科学版）2024，Vol.36Issue(2)：299-306,8.DOI:10.3979/j.issn.1673-825X.202210100273
结合双向LSTM和注意力机制的车辆轨迹预测
Vehicle trajectory prediction combining bidirectional LSTM and attention mechanism
摘要
Abstract
To improve management and service quality in intelligent transportation,autonomous driving and other systems,we propose a bidirectional long short-term memory network combined with self-attention model for vehicle trajectory predic-tion.Our approach adopts Douglas-Pook compression algorithm to compress and pre-process trajectory data,reducing data redundancy and optimizing data processing.The encoder uses a bidirectional long and short-term memory network to fully capture temporal correlation features,while the self-attention mechanism extracts global spatial correlation features from sur-rounding vehicles.The future position of the vehicle is obtained through the fully connected layer of the decoder,and the complete predicted trajectory route is obtained through model iteration.Experimental results show that the prediction per-formance of the proposed model is better than that of the comparison model.In addition,the results of ablation experiments show that the introduction of the trajectory compression algorithm and the improved long short-term memory network com-bined with the attention mechanism have a positive contribution to prediction accuracy.关键词
车辆轨迹/轨迹预测/注意力机制/智能交通Key words
vehicle trajectory/trajectory prediction/attention mechanism/intelligent transportation分类
信息技术与安全科学引用本文复制引用
夏英,熊长江..结合双向LSTM和注意力机制的车辆轨迹预测[J].重庆邮电大学学报（自然科学版）,2024,36(2):299-306,8.基金项目
国家自然科学基金项目(41971365) （41971365）
重庆市教委重点合作项目(HZ2021008) The National Natural Science Foundation of China(41971365) （HZ2021008）
The Chongqing Municipal Education Commission Key Cooperation Projects(HZ2021008) （HZ2021008）
