Trajectory prediction for multiple types of traffic participants at a signalized intersection based on Heterogeneous...

The work is to investigate the trajectory prediction for multiple types of traffic participants in signalized intersection scenarios within intelligent connected environments based on Heterogeneous Spatio-Temporal Multi-Scale Attention Network (HST-MSAN), where participants include Connected and Automated Vehicles (CAVs), Human Vehicles (HVs), cyclists, and pedestrians.

A novel method of trajectory prediction that integrates spatio-temporal interaction features and multi-scale map features is proposed based on HST-MSAN. The interaction model is established based on Spatio-Temporal Graph Attention Network (STGAN). The trajectory prediction model is constructed based on STGAN and Multi-Scale Squeeze-and-Excitation Network (MS-SENet). First, an STGAN is developed to differentially encode the historical trajectory of each participant, model the complex interactions, and quantify the interaction intensity among participants. Second, an MS-SENet that integrates Multi-Scale Convolutional (MSC) and a Squeeze-and-Excitation (SE) module is proposed, where multiple parallel convolutional kernels are employed to extract both local and global map features.

The proposed model is validated through the INTERACTION dataset. The results of three-second trajectory prediction show that the average displacement error (ADE) and final displacement error (FDE) can reach to 0.17 and 0.68 m, respectively. ADE is reduced by 26.1%, 22.7%, 10.5%, and 29.2%, respectively, and FDE is reduced by 10.5%, 12.8%, 8.1%, and 5.6%, respectively, compared with prediction methods of multiple participants of Heterogeneous Edge-enhanced graph attention network (HEAT), Heterogeneous Driving Graph Transformer (HDGT), Hybrid transformer trajectory network (HTTNet), and Flock-inspired network (FN). The ablation experiments show that ADE is reduced by 22.2% and 19.0%, respectively, and FDE is reduced by 10.0% and 5.6%, respectively, compared with the models without STGAN and without MS-SENet.

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.