Evaluation of Large Language Models for Decision Making in Autonomous
  Driving
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.
6577f0d78628ec00e9df0172
Kotaro Tanahashi
claimed_verified
2023-12-18T20:10:54.707Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64ddf4b1e437d02ce68b3efd/9EI93Ag8iTGrIdI5FrOs6.jpeg
Kotaro Tanahashi
user
tanahhh
6577f0d78628ec00e9df0173
Yuichi Inoue
claimed_verified
2023-12-13T08:33:00.117Z
63233b16462470712718c2a3
https://cdn-avatars.huggingface.co/v1/production/uploads/1663253229258-noauth.png
Inoue Yuichi
user
Inoichan
6577f0d78628ec00e9df0174
Yu Yamaguchi
6577f0d78628ec00e9df0175
Hidetatsu Yaginuma
6577f0d78628ec00e9df0176
Daiki Shiotsuka
6577f0d78628ec00e9df0177
Hiroyuki Shimatani
6577f0d78628ec00e9df0178
Kohei Iwamasa
6577f0d78628ec00e9df0179
Yoshiaki Inoue
admin_assigned
2023-12-12T08:43:34.368Z
642ec942ffd6084c6a6118ab
/avatars/8b3003191c1a46a537cf893d2abd5445.svg
Yoshiaki Inoue
user
YoInoue
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Takafumi Yamaguchi
6577f0d78628ec00e9df017b
Koki Igari
6577f0d78628ec00e9df017c
Tsukasa Horinouchi
6577f0d78628ec00e9df017d
Kento Tokuhiro
6577f0d78628ec00e9df017e
Yugo Tokuchi
6577f0d78628ec00e9df017f
Shunsuke Aoki
Evaluation of Large Language Models for Decision Making in Autonomous
  Driving
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.
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.
Large Language Models LLMs autonomous driving spatial recognition spatial-aware decision making traffic rules Proof of Concept POC