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.

Generative Adversarial Models for Vehicular Dynamics Prediction in V2X Networks / Perrone, Giuseppe; Casetti, Claudio; Rapelli, Marco. - (In corso di stampa). (Intervento presentato al convegno 21st International Conference on Network and Service Management CNSM 2025 tenutosi a Bologna (IT) nel 27 - 31 October 2025).

Generative Adversarial Models for Vehicular Dynamics Prediction in V2X Networks

Perrone,Giuseppe;Casetti,Claudio;Rapelli,Marco
In corso di stampa

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3003610