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Computer Science > Robotics

arXiv:2307.07162 (cs)
[Submitted on 14 Jul 2023]

Title:Drive Like a Human: Rethinking Autonomous Driving with Large
Language Models

Authors:Daocheng Fu, Xin Li, Licheng Wen, Min Dou, Pinlong Cai,
Botian Shi, Yu Qiao
Download a PDF of the paper titled Drive Like a Human: Rethinking
Autonomous Driving with Large Language Models, by Daocheng Fu and 6
other authors
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    Abstract:In this paper, we explore the potential of using a large
    language model (LLM) to understand the driving environment in a
    human-like manner and analyze its ability to reason, interpret,
    and memorize when facing complex scenarios. We argue that
    traditional optimization-based and modular autonomous driving
    (AD) systems face inherent performance limitations when dealing
    with long-tail corner cases. To address this problem, we propose
    that an ideal AD system should drive like a human, accumulating
    experience through continuous driving and using common sense to
    solve problems. To achieve this goal, we identify three key
    abilities necessary for an AD system: reasoning, interpretation,
    and memorization. We demonstrate the feasibility of employing an
    LLM in driving scenarios by building a closed-loop system to
    showcase its comprehension and environment-interaction abilities.
    Our extensive experiments show that the LLM exhibits the
    impressive ability to reason and solve long-tailed cases,
    providing valuable insights for the development of human-like
    autonomous driving. The related code are available at this https
    URL .

Subjects: Robotics (cs.RO); Computation and Language (cs.CL)
Cite as:  arXiv:2307.07162 [cs.RO]
          (or arXiv:2307.07162v1 [cs.RO] for this version)
          https://doi.org/10.48550/arXiv.2307.07162
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Licheng Wen [view email]
[v1] Fri, 14 Jul 2023 05:18:34 UTC (2,012 KB)
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