Diffusion Models for Intelligent Transportation Systems: A Survey

TL;DR

This survey reviews the application of diffusion models in Intelligent Transportation Systems, highlighting their theoretical foundations, practical challenges, and diverse applications like autonomous driving and traffic prediction.

Contribution

It provides a comprehensive overview of diffusion models in ITS, covering theoretical aspects, current applications, and future research directions, which is a novel synthesis in this field.

Findings

Diffusion models effectively handle complex, multi-modal traffic data.

They enable controllable generation for traffic simulation and prediction.

Diffusion models show promise in autonomous driving and traffic safety applications.

Abstract

Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing complex challenges within ITS. In this paper, we present a comprehensive survey of diffusion models for ITS, covering both theoretical and practical aspects. First, we introduce the theoretical foundations of diffusion models and their key variants, including conditional diffusion models and latent diffusion models, highlighting their suitability for modeling complex, multi-modal traffic data and enabling controllable generation. Second, we outline the primary challenges in ITS and the corresponding advantages of diffusion models, providing readers with a deeper understanding of the intersection between ITS and diffusion models. Third, we offer a multi-perspective investigation of current applications of diffusion models in ITS domains, including autonomous driving, traffic simulation, trajectory prediction, and traffic safety. Finally, we discuss state-of-the-art diffusion model techniques and highlight key ITS research directions that warrant further investigation. Through this structured overview, we aim to provide researchers with a comprehensive understanding of diffusion models for ITS, thereby advancing their future applications in the transportation domain.