现代信息科技2026，Vol.10Issue(1)：81-85,5.DOI:10.19850/j.cnki.2096-4706.2026.01.016
基于PL-LSTM的高速路段车辆意图识别研究
Research on Vehicle Intention Recognition on Highway Based on PL-LSTM
摘要
Abstract
Driving intention recognition is a key element of Intelligent Transportation Systems(ITS)and autonomous driving decision modules.Although traditional deep learning methods have good performance in data-driven prediction,they lack physical constraints,which easily leads to unreasonable prediction scenarios.Therefore,this paper proposes a method introducing Physics-Informed Neural Networks(PINN)into trajectory prediction,and constructs a PL-LSTM framework fusing Long Short-Term Memory(LSTM).By introducing a physical loss function containing vehicle dynamics,driving behavior priors,and traffic rules,it imposes constraints on the prediction process.Experiments based on the NGSIM dataset confirm that PL-LSTM significantly outperforms baseline methods in terms of accuracy and trajectory error(ADE/FDE).Ablation experiments further analyze the importance of different physical constraints.PL-LSTM can achieve smoother and more reasonable trajectory predictions that conform to traffic regulations.The relevant research conducted in this paper provides new means for intention recognition in multi-agent driving scenarios.关键词
驾驶意图识别/NGSIM/多智能体学习/PINN/LSTM/自动驾驶Key words
driving intention recognition/NGSIM/multi-agent learning/PINN/LSTM/autonomous driving分类
信息技术与安全科学引用本文复制引用
梅元坤..基于PL-LSTM的高速路段车辆意图识别研究[J].现代信息科技,2026,10(1):81-85,5.
