Trajectory prediction for multiple types of traffic participants at a signalized intersection based on Heterogeneous Spatio-Temporal Multi-Scale Attention Network

This model of trajectory prediction jointly models the temporal interaction features of the participants, the spatial interaction features with the surrounding participants, and the multi-scale map features that are most suitable for the current state of the participants. By accurately predicting the future movement trajectories of the surrounding participants, CAVs can identify potential conflict points in advance, optimize their trajectory planning, and reduce the risk of traffic accidents.