🤖 AI Summary
This paper addresses three core challenges in connected and autonomous vehicles (CAVs): low prediction modeling accuracy, insufficient simulation fidelity, and weak decision-making generalizability. To tackle these, it systematically reviews the integration of generative AI—including diffusion models, generative adversarial networks (GANs), large language models (LLMs), and multi-agent simulation—with V2X communication and onboard perception architectures. It identifies three novel technical thrusts: safety-enhanced modeling, synthetic data generation, and digital-twin–physical co-simulation. The work clarifies critical gaps in evaluation metrics and prevailing technical bottlenecks. Furthermore, it proposes a scalable generative-verification closed-loop framework that enables high-fidelity traffic scenario synthesis, robust predictive modeling, and end-to-end closed-loop decision validation. The framework bridges theoretical advances with practical deployment requirements, offering both foundational insights and actionable engineering guidelines for next-generation intelligent transportation systems.
📝 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.