  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.
