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方法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不足。此外，单车自动驾驶系统缺乏与其他车辆的协作与协商能力，而这对于自动驾驶系统的安全性与效率至关重要。为解决这些问题，我们利用大型语言模型提出了一种新型框架AgentsCoDriver，使多车能够实现协同驾驶。AgentsCoDriver包含五个模块：观测模块、推理引擎、认知记忆模块、强化反思模块和通信模块。通过持续与环境交互，它能够逐步积累知识、教训与经验，从而具备终身学习能力。此外，借助通信模块，不同智能体可交换信息，在复杂交通环境中实现协商与协作。大量实验证明了AgentsCoDriver的优越性。