基于PL-LSTM的高速路段车辆意图识别研究OA

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.