Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI

arXiv cs.RO / 4/17/2026

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Key Points

The paper surveys how generative models and Connected and Automated Vehicles (CAVs) are influencing progress in transportation and AI.
It examines where generative models can be applied in CAV contexts to improve predictive modeling, simulation fidelity, and decision-making.
The report discusses both benefits and challenges of combining generative AI with CAV systems, emphasizing practical deployment constraints.
It outlines remaining obstacles while suggesting that the integration could advance both safety and innovation in autonomous driving.
Overall, the study frames the intersection of transportation and AI as an active research area with significant potential but unresolved technical hurdles.

Abstract

This report investigates the history and impact of Generative Models and Connected and Automated Vehicles (CAVs), two groundbreaking forces pushing progress in technology and transportation. By focusing on the application of generative models within the context of CAVs, the study aims to unravel how this integration could enhance predictive modeling, simulation accuracy, and decision-making processes in autonomous vehicles. This thesis discusses the benefits and challenges of integrating generative models and CAV technology in transportation. It aims to highlight the progress made, the remaining obstacles, and the potential for advancements in safety and innovation.