SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914878354096128 |
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| author | Jin, Ye Yang, Ruoxuan Yi, Zhijie Shen, Xiaoxi Peng, Huiling Liu, Xiaoan Qin, Jingli Li, Jiayang Xie, Jintao Gao, Peizhong Zhou, Guyue Gong, Jiangtao |
| author_facet | Jin, Ye Yang, Ruoxuan Yi, Zhijie Shen, Xiaoxi Peng, Huiling Liu, Xiaoan Qin, Jingli Li, Jiayang Xie, Jintao Gao, Peizhong Zhou, Guyue Gong, Jiangtao |
| contents | Leveraging advanced reasoning capabilities and extensive world knowledge of large language models (LLMs) to construct generative agents for solving complex real-world problems is a major trend. However, LLMs inherently lack embodiment as humans, resulting in suboptimal performance in many embodied decision-making tasks. In this paper, we introduce a framework for building human-like generative driving agents using post-driving self-report driving-thinking data from human drivers as both demonstration and feedback. To capture high-quality, natural language data from drivers, we conducted urban driving experiments, recording drivers' verbalized thoughts under various conditions to serve as chain-of-thought prompts and demonstration examples for the LLM-Agent. The framework's effectiveness was evaluated through simulations and human assessments. Results indicate that incorporating expert demonstration data significantly reduced collision rates by 81.04\% and increased human likeness by 50\% compared to a baseline LLM-based agent. Our study provides insights into using natural language-based human demonstration data for embodied tasks. The driving-thinking dataset is available at \url{https://github.com/AIR-DISCOVER/Driving-Thinking-Dataset}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13193 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data Jin, Ye Yang, Ruoxuan Yi, Zhijie Shen, Xiaoxi Peng, Huiling Liu, Xiaoan Qin, Jingli Li, Jiayang Xie, Jintao Gao, Peizhong Zhou, Guyue Gong, Jiangtao Human-Computer Interaction H.5.2 Leveraging advanced reasoning capabilities and extensive world knowledge of large language models (LLMs) to construct generative agents for solving complex real-world problems is a major trend. However, LLMs inherently lack embodiment as humans, resulting in suboptimal performance in many embodied decision-making tasks. In this paper, we introduce a framework for building human-like generative driving agents using post-driving self-report driving-thinking data from human drivers as both demonstration and feedback. To capture high-quality, natural language data from drivers, we conducted urban driving experiments, recording drivers' verbalized thoughts under various conditions to serve as chain-of-thought prompts and demonstration examples for the LLM-Agent. The framework's effectiveness was evaluated through simulations and human assessments. Results indicate that incorporating expert demonstration data significantly reduced collision rates by 81.04\% and increased human likeness by 50\% compared to a baseline LLM-based agent. Our study provides insights into using natural language-based human demonstration data for embodied tasks. The driving-thinking dataset is available at \url{https://github.com/AIR-DISCOVER/Driving-Thinking-Dataset}. |
| title | SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data |
| topic | Human-Computer Interaction H.5.2 |
| url | https://arxiv.org/abs/2309.13193 |