Towards Interactive and Learnable Cooperative Driving Automation: A Large Language Model-Driven Decision-Making Framework

Connected Autonomous Vehicles (CAVs) are being tested globally, but their performance in complex scenarios remains suboptimal. While cooperative driving improves CAV performance by leveraging vehicle collaboration, its lack of interaction and continuous learning limits current applications to single scenarios and specific Cooperative Driving Automation (CDA). To address these issues, this paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework for all-scenario and all-CDA applications. First, an environment module updates vehicle positions based on semantic decisions, mitigating errors from LLM-controlled positioning. Second, leveraging the four CDA levels defined in SAE J3216, a centralized-distributed coupled architecture reasoning module is developed to ensure safe and efficient cooperation through centralized negotiation and distributed decision. Finally, by introducing a memory module that employs Retrieval Augmented Generation (RAG), CAVs are endowed with the ability to learn from their past experiences to avoid repeating mistakes. Through ablation studies and comparisons with other cooperative driving methods, the results demonstrate that the proposed CoDrivingLLM significantly enhances safety, efficiency, and adaptability across various scenarios.