Graph Attention Mechanism-based Method for Trajectory Prediction in Map-Free Scenes
Existing trajectory prediction methods rely heavily on high-definition maps,which are time-consuming,costly,and complex to acquire.This makes it difficult for them to quickly adapt to the widespread adoption of intelligent transportation.To address the problem of vehicle trajectory prediction in map-free scenes,a trajectory prediction method based on spatio-temporal features of multi-modal data is proposed in this paper.Multiple spatio-temporal interaction graphs are constructed from the history of the trajectory,temporal and spatial attention are cross-utilized and deeply fused to model the spatio-temporal correlations between vehicles on the road.Finally,a residual network is used for a multi-objective and multi-modal trajectory generation.The model is trained and tested on the real dataset,Argoverse 2,and the experimental results show that compared with the CRAT-Pred,this model can improve minADE,minFDE and Miss Rate(MR)metrics in single-modal prediction by 3.86%,3.89%,and 0.48%,and in multi-modal prediction by 0.78%,0.96%and 0.42%.Hence,the proposed trajectory prediction method can efficiently capture the temporal and spatial characteristics of vehicle movement trajectories and can be effectively applied in related fields such as autonomous driving.
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