Connected and autonomous driving is developing rapidly in recent years. However, current autonomous driving systems, which are primarily based on data-driven approaches, exhibit deficiencies in interpretability, generalization, and continuing learning capabilities. In addition, the single-vehicle autonomous driving systems lack of the ability of collaboration and negotiation with other vehicles, which is crucial for the safety and efficiency of autonomous driving systems. In order to address these issues, we leverage large language models (LLMs) to develop a novel framework, AgentsCoDriver, to enable multiple vehicles to conduct collaborative driving. AgentsCoDriver consists of five modules: observation module, reasoning engine, cognitive memory module, reinforcement reflection module, and communication module. It can accumulate knowledge, lessons, and experiences over time by continuously interacting with the environment, thereby making itself capable of lifelong learning. In addition, by leveraging the communication module, different agents can exchange information and realize negotiation and collaboration in complex traffic environments. Extensive experiments are conducted and show the superiority of AgentsCoDriver.

翻译：近年来，网联自动驾驶技术发展迅速。然而，当前主要基于数据驱动的自动驾驶系统在可解释性、泛化能力和持续学习能力方面存在明显不足。此外，单车自动驾驶系统缺乏与其它车辆协作与协商的能力，而这恰恰是保障自动驾驶系统安全性与效率的关键。为应对上述问题，我们利用大语言模型（LLMs）开发了新型框架AgentsCoDriver，使多车辆能够实现协同驾驶。AgentsCoDriver由五大模块构成：观测模块、推理引擎、认知记忆模块、强化反射模块和通信模块。通过持续与环境交互，该框架能够逐步积累知识、教训与经验，从而实现终身学习能力。同时，借助通信模块，不同智能体可在复杂交通环境中交换信息，实现协商与协作。大量实验证明了AgentsCoDriver的优越性。