LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866912327091093504 |
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| author | Sha, Hao Mu, Yao Jiang, Yuxuan Chen, Li Xu, Chenfeng Luo, Ping Li, Shengbo Eben Tomizuka, Masayoshi Zhan, Wei Ding, Mingyu |
| author_facet | Sha, Hao Mu, Yao Jiang, Yuxuan Chen, Li Xu, Chenfeng Luo, Ping Li, Shengbo Eben Tomizuka, Masayoshi Zhan, Wei Ding, Mingyu |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03026 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving Sha, Hao Mu, Yao Jiang, Yuxuan Chen, Li Xu, Chenfeng Luo, Ping Li, Shengbo Eben Tomizuka, Masayoshi Zhan, Wei Ding, Mingyu Robotics Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning 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 |
| title | LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving |
| topic | Robotics Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2310.03026 |