Improving Multi-Agent Trajectory Prediction Using Traffic States on Interactive Driving Scenarios

Abstract

Predicting trajectories of multiple agents in interactive driving scenarios such as intersections, and roundabouts are challenging due to the high density of agents, varying speeds, and environmental obstacles. Existing approaches use relative distance and semantic maps of intersections to improve trajectory prediction. However, drivers base their driving decision on the overall traffic state of the intersection and the surrounding vehicles. So, we propose to use traffic states that denote changing spatio-temporal interaction between neighboring vehicles, to improve trajectory prediction. An example of a traffic state is a clump state which denotes that the vehicles are moving close to each other, i.e., congestion is forming. We develop three prediction models with different architectures, namely, Transformer-based (TS-Transformer), Generative Adversarial Network-based (TS-GAN), and Conditional Variational Autoencoder-based (TS-CVAE). We show that traffic state-based models consistently predict better future trajectories than the vanilla models. TS-Transformer produces state-of-the-art results on two challenging interactive trajectory prediction datasets, namely, Eye-on-Traffic (EOT), and INTERACTION. Our qualitative analysis shows that traffic state-based models have better aligned trajectories to the ground truth.