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

arXiv:2404.11181v2 Announce Type: replace
Abstract: 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 …

abstract adversarial arxiv autonomous autonomous driving autonomous driving systems challenges cs.ai cs.lg cs.ro driving forecasting gan generative generative adversarial networks however interactions knowledge management networks prediction signal systems traffic traffic management trajectory type types unique urban