Cui, Can (Purdue University), Yang, Zichong (Purdue University), Zhou, Yupeng (Purdue University), Ma, Yunsheng (Purdue University), Lu, Juanwu (Purdue University), Li, Lingxi (Purdue University), Chen, Yaobin (Purdue School of Engineering and Technology, IUPUI), Panchal, Jitesh (Purdue University), Wang, Ziran (Purdue University)

Personalized Autonomous Driving with Large Language Models: Field Experiments

Scheduled for presentation during the Invited Session "Learning-empowered Intelligent Transportation Systems: Foundation Vehicles and Coordination Technique I" (WeAT1), Wednesday, September 25, 2024, 11:50−12:10, Salon 1

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Integrating large language models (LLMs) in autonomous vehicles enables conversation with AI systems to drive the vehicle. However, it also emphasizes the requirement for such systems to comprehend commands accurately and achieve higher-level personalization to adapt to the preferences of drivers or passengers over a more extended period. In this paper, we introduce an LLM-based framework, Talk2Drive, capable of translating natural verbal commands into executable controls and learning to satisfy personal preferences for safety, efficiency, and comfort with a proposed memory module. This is the first-of-its-kind multi-scenario field experiment that deploys LLMs on a real-world autonomous vehicle. Experiments showcase that the proposed system can comprehend human intentions at different intuition levels, ranging from direct commands like "can you drive faster" to indirect commands like "I am really in a hurry now". Additionally, we use the takeover rate to quantify the trust of human drivers in the LLM-based autonomous driving system, where Talk2Drive significantly reduces the takeover rate in highway, intersection, and parking scenarios. We also validate that the proposed memory module considers personalized preferences and further reduces the takeover rate by up to 65.2% compared with those without a memory module.