ConnectGPT: Connect Large Language Models with Connected and Automated Vehicles

This paper explores the intersection of recent AI advancements and Intelligent Transportation Systems (ITS), specifically focusing on enhancing the capabilities of Connected and Automated Vehicles (CAVs) in dynamic traffic scenarios. While combinations of vehicular sensors and AI offer promising prospects for advanced environmental perception, challenges still persist in accurately identifying dangers during the transition to automated traffic. The ESRIUM project, funded by the EU Horizon 2020 Programme, aims to address these challenges by developing digital maps representing road deterioration and employing Vehicle-to-Everything (V2X) communication to generate infrastructure-assisted routing recommendations for CAVs. While the solutions for sending standardized safety messages and controlling enabled CAVs were demonstrated in the ESRIUM project, the solution for the automatic generation of Cooperative Intelligent Transport Systems (C-ITS) safety messages was not studied. In this paper, we propose a pipeline named "ConnectGPT", which connects Large Language Models (LLMs) with CAVs, utilizing GPT-4, to observe traffic conditions, identify conditions that can endanger the flow of traffic, and automate the generation of the corresponding standardized C-ITS messages, such as Decentralized Environmental Notification Message (DENM) about the actual safety problem. Practical experiments with ongoing development show potential for real-world applications, which can significantly improve traffic management efficiency and enhance the security of all traffic participants, marking a crucial advancement in the integration of AI tools in ITS.