LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
This work addresses the problem of improving safety, efficiency, and generalizability in autonomous driving for real-world applications, representing an incremental step by integrating LLMs into existing frameworks.
The paper tackles challenges in autonomous driving by using Large Language Models (LLMs) as decision-makers for complex scenarios, demonstrating that the method consistently surpasses baseline approaches in tasks like single-vehicle and multi-vehicle coordination.
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To address these problems, this work employs Large Language Models (LLMs) as a decision-making component for complex AD scenarios that require human commonsense understanding. We devise cognitive pathways to enable comprehensive reasoning with LLMs, and develop algorithms for translating LLM decisions into actionable driving commands. Through this approach, LLM decisions are seamlessly integrated with low-level controllers by guided parameter matrix adaptation. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination, thanks to the commonsense reasoning capabilities of LLMs. This paper presents an initial step toward leveraging LLMs as effective decision-makers for intricate AD scenarios in terms of safety, efficiency, generalizability, and interoperability. We aspire for it to serve as inspiration for future research in this field. Project page: https://sites.google.com/view/llm-mpc