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.| File | Dimensione | Formato | |
|---|---|---|---|
|
Generative Adversarial Models for Vehicular Dynamics Prediction in V2X Networks.pdf
accesso aperto
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
600.29 kB
Formato
Adobe PDF
|
600.29 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/3003610
