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 \href{https://github.com/JohnsonJiang1996/Awesome-VLA4AD}{SicongJiang/Awesome-VLA4AD}.
@article{arxiv.2506.24044,
title = {A Survey on Vision-Language-Action Models for Autonomous Driving},
author = {Sicong Jiang and Zilin Huang and Kangan Qian and Ziang Luo and Tianze Zhu and Yang Zhong and Yihong Tang and Menglin Kong and Yunlong Wang and Siwen Jiao and Hao Ye and Zihao Sheng and Xin Zhao and Tuopu Wen and Zheng Fu and Sikai Chen and Kun Jiang and Diange Yang and Seongjin Choi and Lijun Sun},
journal= {arXiv preprint arXiv:2506.24044},
year = {2025}
}