Generative Adversarial Models for Vehicular Dynamics Prediction in V2X Networks

Real-time trajectory prediction is critical for safe and efficient operation of connected and autonomous vehicles (CAVs). Yet existing deterministic models struggle to capture the multi-modal, uncertain nature of traffic evolution. This paper addresses this gap by proposing a framework powered by Generative Adversarial Networks (GANs) that uses V2X (Vehicle To Everything) data to create a digital twin capable of simulating plausible future scenarios and of supporting planning and decision-making in CAVs. The Generative framework is designed to generate multiple trajectory predictions conditioned on received CAM (Cooperative Awareness Message) data, using a Transformer-based architecture with temporal consistency regularization. We evaluate it in a SUMO-simulated environment, demonstrating improved stability and realism over baseline GAN training.