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

TL;DR

This survey explores how generative models are integrated into connected and automated vehicles (CAVs), highlighting their potential to improve predictive capabilities, simulation, and decision-making in autonomous transportation.

Contribution

It provides a comprehensive overview of the intersection between generative AI models and CAV technology, emphasizing current progress, challenges, and future opportunities.

Findings

Generative models can enhance simulation accuracy for CAVs.

Integration of generative models can improve decision-making in autonomous vehicles.

The study identifies key challenges and future directions in this interdisciplinary field.

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.