KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections

Chuheng Wei, Guoyuan Wu, Matthew J. Barth, Amr Abdelraouf, Rohit Gupta, Kyungtae Han; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7115-7124

Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However it presents unique challenges due to the complex roadway layout at intersections involvement of traffic signal controls and interactions among different types of road users. To address these issues we present in this paper a novel model called Knowledge-Informed Generative Adversarial Network (KI-GAN) which integrates both traffic signal information and multi-vehicle interactions to predict vehicle trajectories accurately. Additionally we propose a specialized attention pooling method that accounts for vehicle orientation and proximity at intersections. Based on the SinD dataset our KI-GAN model is able to achieve an Average Displacement Error (ADE) of 0.05 and a Final Displacement Error (FDE) of 0.12 for a 6-second observation and 6-second prediction cycle. When the prediction window is extended to 9 seconds the ADE and FDE values are further reduced to 0.11 and 0.26 respectively. These results demonstrate the effectiveness of the proposed KI-GAN model in vehicle trajectory prediction under complex scenarios at signalized intersections which represents a significant advancement in the target field.