Receive, Reason, and React: Drive as You Say, With Large Language Models in Autonomous Vehicles

The fusion of human-centric design and artificial intelligence capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond traditional transportation. These vehicles can dynamically interact with passengers and adapt to their preferences. This article proposes a novel framework that leverages large language models (LLMs) to enhance the decision-making process in autonomous vehicles. By utilizing LLMs’ contextual understanding abilities with specialized tools, we aim to integrate the language and reasoning capabilities of LLMs into autonomous vehicles. Our research includes experiments in HighwayEnv, a collection of environments for autonomous driving and tactical decision-making tasks, to explore LLMs’ interpretation, interaction, and reasoning in various scenarios. We also examine some well-defined real-time personalized driving tasks, demonstrating how LLMs can influence driving behaviors based on drivers’ verbal commands. Our empirical results highlight the substantial advantages of utilizing chain-of-thought prompting, leading to improved driving decisions and showing the potential for LLMs to enhance personalized driving experiences through ongoing verbal feedback. The proposed framework aims to transform autonomous vehicle operations, offering personalized support, transparent decision making, and continuous learning to enhance safety and effectiveness. We achieve user-centric, transparent, and adaptive autonomous driving ecosystems supported by the integration of LLMs into autonomous vehicles. Experiment videos are available at
https://youtube.com/playlist?list=PLgcRcf9w8BmLJi_fqTGq-7KCZsbpEIE4a&si=dhH9lgaeSmB5K94t
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