A Survey on Vision-Language-Action Models for Autonomous Driving
Authors: name: Sicong Jiang; name: Zilin Huang; name: Kangan Qian; name: Ziang Luo; name: Tianze Zhu; name: Yang Zhong; name: Yihong Tang; name: Menglin Kong; name: Yunlong Wang; name: Siwen Jiao; name: Hao Ye; name: Zihao Sheng; name: Xin Zhao; name: Tuopu Wen; name: Zheng Fu; name: Sikai Chen; name: Kun Jiang; name: Diange Yang; name: Seongjin Choi; name: Lijun Sun
Published: 2025-06-30 16:50:02

Abstract: The rapid progress of multimodal large language models (MLLM) has paved the
way for Vision-Language-Action (VLA) paradigms, which integrate visual
perception, natural language understanding, and control within a single policy.
Researchers in autonomous driving are actively adapting these methods to the
vehicle domain. Such models promise autonomous vehicles that can interpret
high-level instructions, reason about complex traffic scenes, and make their
own decisions. However, the literature remains fragmented and is rapidly
expanding. This survey offers the first comprehensive overview of VLA for
Autonomous Driving (VLA4AD). We (i) formalize the architectural building blocks
shared across recent work, (ii) trace the evolution from early explainer to
reasoning-centric VLA models, and (iii) compare over 20 representative models
according to VLA's progress in the autonomous driving domain. We also
consolidate existing datasets and benchmarks, highlighting protocols that
jointly measure driving safety, accuracy, and explanation quality. Finally, we
detail open challenges - robustness, real-time efficiency, and formal
verification - and outline future directions of VLA4AD. This survey provides a
concise yet complete reference for advancing interpretable socially aligned
autonomous vehicles. Github repo is available at
https://github.com/JohnsonJiang1996/Awesome-VLA4AD{SicongJiang/Awesome-VLA4AD}.