Leveraging Generative AI for Smarter and Safer Urban Transportation: A Holistic View

Qiyang Zhao, Anuj Abraham, Hang Zou

Abstract: The potential impact of generative AI (GenAI) on urban transportation is vast and significant. This chapter explores a future where diverse mobile agents, empowered by GenAI models, collaborate efficiently through ITSs to achieve a variety of objectives. GenAI has the potential to enhance decision-making for vehicles and infrastructure, leading to safer roads and smoother traffic patterns. It has the potential to generate realistic driving and traffic scenarios. Additionally, GenAI can assist agents in predicting outcomes and planning actions without direct real-world interaction, thus improving the reliability and adaptability of machine learning (ML)-based ITS. This chapter also explores large multi-modal models (LMMs) and their potential applications in the ITS represent a promising frontier in this landscape, providing advanced abilities to interpret and generate diverse multimodal data inputs. Rather than seeing GenAI as a substitute for existing ML algorithms, we consider it as a valuable addition, offering advanced capabilities in abstraction, planning, and reasoning, which can evolve conventional systems from autonomous to cognition.

External IDs:doi:10.1007/978-3-031-72959-1_3