Using Large Language Models in Autonomous Driving Systems

The rapid advancement of Large Language Models (LLMs) has opened new avenues for their integration into autonomous driving systems (ADS). This paper provides a comprehensive literature review of the emerging role of LLMs in ADS, focusing on high-level strategic functions such as route planning, scene understanding, human-vehicle interaction, and safety analysis. We analyse over 40 recent studies and categorise the application of LLMs into five key domains: strategic planning and reasoning, multimodal environment perception, human-LLM interaction and personalisation, risk prediction and safety enhancement, and technical limitations of real-time LLM integration. Our findings suggest that while LLMs excel in semantic interpretation and explainable decision-making, they face significant challenges in real-time control due to latency, inconsistency, and unverifiable outputs. As such, their use should be restricted to strategic-level components where human oversight and higher response tolerances are acceptable. Based on these insights, we outline four significant research gaps related to real-world validation, interface standardisation, multimodal fusion, and risk reasoning. We recommend hybrid architectures and inference optimisation as promising directions for the future development of safe and scalable LLM-enhanced autonomous transport systems.