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<!doctype html>
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<title>Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future</title>
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<h1>
Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
</h1>
<h2 style="margin:2.2rem 0 1.8rem; display:flex; align-items:center; gap:0.4rem;">
<span class="icon is-small">
<img src="assets_common/icons/worldbench-circle.png" style="height:1.8em; vertical-align:text-top;">
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</h2>
<p class="wb-kicker" style="margin:0.5rem 0 1.2rem;">Manuscript and collection updated: October 16, 2026.</p>
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<a href="https://arxiv.org/abs/2512.16760" class="button paper-link" target="_blank">
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<h4>Contents</h4>
<div><a href="#definition">Definition</a></div>
<!-- <div><a href="#taxonomy">Taxonomy</a></div> -->
<div><a href="#projects">Collections</a></div>
<div><a href="#citation">Citation</a></div>
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<section id="introduction">
<div class="wb-section">
<div class="wb-kicker"><span class="icon is-small">
<img src="assets/vla4ad/figures/intro.png" style="height: 1.6em;" loading="lazy">
</span> Introduction</div>
<div class="wb-hr"></div>
<p class="wb-lead">
The pursuit of fully autonomous driving (AD) has long been a central goal in AI and robotics.
Conventional AD systems typically adopt a modular "Perception-Decision-Action" pipeline, where mapping, object detection, motion prediction, and trajectory planning are developed and optimized as separate components.
</p>
<p class="wb-lead">
While this design has achieved strong performance in structured environments, its reliance on hand-crafted interfaces and rules limits adaptability in complex, dynamic, and long-tailed scenarios.
</p>
<figure style="width: 100%; max-width: 1000px; display: block; margin: 24px auto;">
<img src="assets/vla4ad/figures/teaser.png">
</figure>
<p class="wb-lead">
This survey reviews <strong>Vision-Language-Action (VLA) models</strong> — an emerging paradigm that integrates
visual perception, natural language reasoning, and executable actions for autonomous driving.
We trace the evolution from traditional <strong>Vision-Action (VA) approaches</strong> to <strong>modern VLA frameworks</strong>.
Charting the evolution from precursor VA models to modern VLA frameworks, we provide historical context and clarify the motivations behind this paradigm shift.
</p>
<br><br>
</div>
</section>
<section id="definition">
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</span> Definition</div>
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<div class="wb-grid">
<!-- Vision-Action (VA) Models -->
<article class="wb-card" id="va-models">
<h3>
<i class="fas fa-film" aria-hidden="true"></i>
<a class="wb-anchor">Vision-Action (VA) Models</a>
</h3>
<p>
A vision-centric driving system that directly maps raw sensory
observations to driving actions, thereby avoiding explicit modular decomposition
into perception, prediction, and planning. VA models learn end-to-end policies through imitation learning
or reinforcement learning.
</p>
<!-- <p class="wb-cap">
<strong>Typical inputs</strong>: Multi-View RGB Images, LiDAR Point Clouds, Ego Vehicle State, Maps.
</p> -->
<div class="wb-meta" aria-label="VA Models tags">
<span class="wb-chip">End-to-End Models</span>
<span class="wb-chip">World Models</span>
<span class="wb-chip">Imitation Learning</span>
<span class="wb-chip">Reinforcement Learning</span>
<span class="wb-chip">Trajectory Prediction</span>
</div>
</article>
<!-- Vision-Language-Action (VLA) Models -->
<article class="wb-card" id="lidargen">
<h3>
<i class="fas fa-braille" aria-hidden="true"></i>
<a class="wb-anchor">Vision-Language-Action (VLA) Models</a>
</h3>
<p>
A multimodal reasoning system that couples visual perception with
large VLMs to produce executable driving actions. VLAs integrate visual understanding,
linguistic reasoning, and actionable outputs within a unified framework, enabling more interpretable,
generalizable, and human-aligned driving policies through natural language instructions and chain-of-thought reasoning.
</p>
<!-- <p class="wb-cap">
<strong>Typical inputs</strong>: Multi-View RGB Images, LiDAR Point Clouds, BEV Features, Natural Language Instructions,
Scene Descriptions, Traffic Rules, Ego Status, Context Demonstrations.
</p> -->
<div class="wb-meta" aria-label="VLA Models tags">
<span class="wb-chip">End-to-End VLA</span>
<span class="wb-chip">Dual-System VLA</span>
<span class="wb-chip">Chain-of-Thought</span>
<span class="wb-chip">Instruction Following</span>
<span class="wb-chip">Interpretable Reasoning</span>
</div>
</article>
</div>
<br><br>
</section>
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<section id="projects">
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</span> Collections</div>
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</section>
<section id="va-papers" class="papers-section" data-section-key="va" data-initial="3">
<h2>Vision-Action (VA) Models</h2>
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<h2>Vision-Language-Action (VLA) Models</h2>
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<pre style="overflow-x:auto; padding:1.2rem; background:#f7f8fa; border-radius:8px;"><code>@article{survey_vla4ad,
title = {Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future},
author = {Tianshuai Hu and Xiaolu Liu and Song Wang and Yiyao Zhu and Ao Liang and Lingdong Kong and Guoyang Zhao and Zeying Gong and Jun Cen and Zhiyu Huang and Xiaoshuai Hao and Linfeng Li and Hang Song and Xiangtai Li and Jun Ma and Shaojie Shen and Jianke Zhu and Dacheng Tao and Ziwei Liu and Junwei Liang},
journal = {arXiv preprint arXiv:2512.16760},
year = {2025},
}</code></pre>
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