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<h1 class="title is-1 publication-title">A Language Agent for Autonomous Driving</h1>
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<a href="https://PointsCoder.github.io">Jiageng Mao</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://jay-ye.github.io/">Junjie Ye</a><sup>1*</sup>,</span>
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<a href="https://www.linkedin.com/in/yuxiqian">Yuxi Qian</a><sup>1</sup>,</span>
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<a href="https://web.stanford.edu/~pavone/index.html">Marco Pavone</a><sup>2,3</sup>,
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<span class="author-block">
<a href="https://yuewang.xyz">Yue Wang</a><sup>1,3</sup>
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<span class="author-block"><sup>1</sup>University of Southern California,</span>
<span class="author-block"><sup>2</sup>Stanford University,</span>
<span class="author-block"><sup>3</sup>NVIDIA</span>
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<span class="dnerf">Agent-Driver</span> transforms the conventional perception-prediction-planning framework by introducing LLMs as an agent for autonomous driving.
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Human-level driving is an essential goal of autonomous driving. Conventional approaches formulate autonomous driving as a perception-prediction-planning framework, yet their systems do not capitalize on the inherent reasoning ability and experiential knowledge of humans. In this paper, we propose a fundamental paradigm shift from current pipelines, exploiting Large Language Models (LLMs) as a cognitive agent to integrate human-like intelligence into autonomous driving systems. Our system, termed Agent-Driver, transforms the traditional autonomous driving pipeline by introducing a versatile tool library accessible via function calls, a cognitive memory of common sense and experiential knowledge for decision-making, and a reasoning engine capable of chain-of-thought reasoning, task planning, motion planning, and self-reflection. Powered by LLMs, our Agent-Driver is endowed with intuitive common sense and robust reasoning capabilities, thus enabling a more nuanced, human-like approach to autonomous driving. We evaluate our system on the large-scale nuScenes benchmark, and extensive experiments substantiate that our Agent-Driver significantly outperforms the state-of-the-art driving methods by a large margin. Our approach also demonstrates superior interpretability and few-shot learning ability to these methods.
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</p><li>
We present Agent-Driver, an LLM-powered agent that revolutionizes the traditional perception-prediction-planning framework, establishing a powerful yet flexible paradigm for human-like autonomous driving.
</li>
<li>
Agent-Driver integrates a tool library for dynamic perception and prediction, a cognitive memory for human knowledge, and a reasoning engine that emulates human decision-making, all orchestrated by LLMs to enable a more anthropomorphic autonomous driving process.
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Agent-Driver significantly outperforms the state-of-the-art autonomous driving systems by a large margin, with over $30\%$ collision improvements in motion planning. Our approach also demonstrates strong few-shot learning ability and interpretability on the nuScenes benchmark.
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We provide a variety range of ablation study to dissect the proposed architecture and understand the efficacy of each module, to facilitate future research in this direction.
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<pre><code>@article{mao2023agentdriver,
author = {Mao, Jiageng and Ye, Junjie and Qian, Yuxi and Pavone, Marco and Wang, Yue},
title = {A Language Agent for Autonomous Driving},
year = {2023},
}</code></pre>
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