Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of the deep neural networks within the AD system, particularly in out-of-distribution and uncertain data. To this end, this paper explores the integration of Large Language Models (LLMs) into AD systems, leveraging their robust common-sense knowledge and reasoning abilities. The proposed methodologies employ LLMs as intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning, for enhancing driving performance and safety. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine. Demonstrating superior performance and safety metrics compared to state-of-the-art approaches, our approach shows the promising potential for using LLMs for autonomous vehicles.

翻译：自动驾驶在长尾未预见驾驶场景中面临显著的安全挑战，这主要源于自动驾驶系统中深度神经网络的可解释性差和泛化能力不足，特别是在分布外数据和不确定数据的情况下。为此，本文探索将大语言模型整合到自动驾驶系统中，利用其强大的常识知识和推理能力。所提出的方法采用大语言模型作为行为规划中的智能决策者，并辅以安全验证器屏障进行上下文安全学习，以提升驾驶性能和安全性。我们在模拟环境中进行了两项关键研究：一种自适应大语言模型条件化模型预测控制，以及一种含状态机的大语言模型驱动的交互式行为规划方案。与最先进的方法相比，我们的方法在性能和安全性指标上表现更优，展示了利用大语言模型进行自动驾驶的有前景潜力。