Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles
September 19, 2023 ยท Declared Dead ยท ๐ 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
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Authors
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
arXiv ID
2309.10228
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI
Citations
157
Venue
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Last Checked
2 months ago
Abstract
The future of autonomous vehicles lies in the convergence of human-centric design and advanced AI capabilities. Autonomous vehicles of the future will not only transport passengers but also interact and adapt to their desires, making the journey comfortable, efficient, and pleasant. In this paper, we present a novel framework that leverages Large Language Models (LLMs) to enhance autonomous vehicles' decision-making processes. By integrating LLMs' natural language capabilities and contextual understanding, specialized tools usage, synergizing reasoning, and acting with various modules on autonomous vehicles, this framework aims to seamlessly integrate the advanced language and reasoning capabilities of LLMs into autonomous vehicles. The proposed framework holds the potential to revolutionize the way autonomous vehicles operate, offering personalized assistance, continuous learning, and transparent decision-making, ultimately contributing to safer and more efficient autonomous driving technologies.
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