title: Evaluation of Large Language Models for Decision Making in Autonomous
    Driving
summary: Various methods have been proposed for utilizing Large Language Models (LLMs)
  in autonomous driving. One strategy of using LLMs for autonomous driving
  involves inputting surrounding objects as text prompts to the LLMs, along with
  their coordinate and velocity information, and then outputting the subsequent
  movements of the vehicle. When using LLMs for such purposes, capabilities such
  as spatial recognition and planning are essential. In particular, two
  foundational capabilities are required: (1) spatial-aware decision making,
  which is the ability to recognize space from coordinate information and make
  decisions to avoid collisions, and (2) the ability to adhere to traffic rules.
  However, quantitative research has not been conducted on how accurately
  different types of LLMs can handle these problems. In this study, we
  quantitatively evaluated these two abilities of LLMs in the context of
  autonomous driving. Furthermore, to conduct a Proof of Concept (POC) for the
  feasibility of implementing these abilities in actual vehicles, we developed a
  system that uses LLMs to drive a vehicle.
paper_authors._id: 6577f0d78628ec00e9df0172
paper_authors.name: Kotaro Tanahashi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-18T20:10:54.707Z
paper_authors._id: 64ddf4b1e437d02ce68b3efd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ddf4b1e437d02ce68b3efd/9EI93Ag8iTGrIdI5FrOs6.jpeg
paper_authors.fullname: Kotaro Tanahashi
paper_authors.type: user
paper_authors.user: tanahhh
paper_authors._id: 6577f0d78628ec00e9df0173
paper_authors.name: Yuichi Inoue
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-13T08:33:00.117Z
paper_authors._id: 63233b16462470712718c2a3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1663253229258-noauth.png
paper_authors.fullname: Inoue Yuichi
paper_authors.type: user
paper_authors.user: Inoichan
paper_authors._id: 6577f0d78628ec00e9df0174
paper_authors.name: Yu Yamaguchi
paper_authors._id: 6577f0d78628ec00e9df0175
paper_authors.name: Hidetatsu Yaginuma
paper_authors._id: 6577f0d78628ec00e9df0176
paper_authors.name: Daiki Shiotsuka
paper_authors._id: 6577f0d78628ec00e9df0177
paper_authors.name: Hiroyuki Shimatani
paper_authors._id: 6577f0d78628ec00e9df0178
paper_authors.name: Kohei Iwamasa
paper_authors._id: 6577f0d78628ec00e9df0179
paper_authors.name: Yoshiaki Inoue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-12T08:43:34.368Z
paper_authors._id: 642ec942ffd6084c6a6118ab
paper_authors.avatarUrl: /avatars/8b3003191c1a46a537cf893d2abd5445.svg
paper_authors.fullname: Yoshiaki Inoue
paper_authors.type: user
paper_authors.user: YoInoue
paper_authors._id: 6577f0d78628ec00e9df017a
paper_authors.name: Takafumi Yamaguchi
paper_authors._id: 6577f0d78628ec00e9df017b
paper_authors.name: Koki Igari
paper_authors._id: 6577f0d78628ec00e9df017c
paper_authors.name: Tsukasa Horinouchi
paper_authors._id: 6577f0d78628ec00e9df017d
paper_authors.name: Kento Tokuhiro
paper_authors._id: 6577f0d78628ec00e9df017e
paper_authors.name: Yugo Tokuchi
paper_authors._id: 6577f0d78628ec00e9df017f
paper_authors.name: Shunsuke Aoki
paper_title: Evaluation of Large Language Models for Decision Making in Autonomous
    Driving
paper_summary: Various methods have been proposed for utilizing Large Language Models (LLMs)
  in autonomous driving. One strategy of using LLMs for autonomous driving
  involves inputting surrounding objects as text prompts to the LLMs, along with
  their coordinate and velocity information, and then outputting the subsequent
  movements of the vehicle. When using LLMs for such purposes, capabilities such
  as spatial recognition and planning are essential. In particular, two
  foundational capabilities are required: (1) spatial-aware decision making,
  which is the ability to recognize space from coordinate information and make
  decisions to avoid collisions, and (2) the ability to adhere to traffic rules.
  However, quantitative research has not been conducted on how accurately
  different types of LLMs can handle these problems. In this study, we
  quantitatively evaluated these two abilities of LLMs in the context of
  autonomous driving. Furthermore, to conduct a Proof of Concept (POC) for the
  feasibility of implementing these abilities in actual vehicles, we developed a
  system that uses LLMs to drive a vehicle.
paper_ai_summary: A study evaluates Large Language Models' abilities in spatial recognition and traffic rule adherence for autonomous driving, with a Proof of Concept system driving a vehicle.
paper_ai_keywords: Large Language Models LLMs autonomous driving spatial recognition spatial-aware decision making traffic rules Proof of Concept POC