A Survey on Vision-Language-Action Models for Autonomous Driving
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}.
68681d82213f123a1f88b973
Sicong Jiang
claimed_verified
2025-07-05T07:52:40.893Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/683daabc402acb18654e4674/zHLWdgjkY9GvvEEYroHVQ.jpeg
Sicong Jiang
user
Jo1nsonJiang
68681d82213f123a1f88b974
Zilin Huang
68681d82213f123a1f88b975
Kangan Qian
68681d82213f123a1f88b976
Ziang Luo
68681d82213f123a1f88b977
Tianze Zhu
68681d82213f123a1f88b978
Yang Zhong
68681d82213f123a1f88b979
Yihong Tang
68681d82213f123a1f88b97a
Menglin Kong
68681d82213f123a1f88b97b
Yunlong Wang
68681d82213f123a1f88b97c
Siwen Jiao
68681d82213f123a1f88b97d
Hao Ye
68681d82213f123a1f88b97e
Zihao Sheng
68681d82213f123a1f88b97f
Xin Zhao
68681d82213f123a1f88b980
Tuopu Wen
68681d82213f123a1f88b981
Zheng Fu
68681d82213f123a1f88b982
Sikai Chen
68681d82213f123a1f88b983
Kun Jiang
68681d82213f123a1f88b984
Diange Yang
68681d82213f123a1f88b985
Seongjin Choi
68681d82213f123a1f88b986
Lijun Sun
A Survey on Vision-Language-Action Models for Autonomous Driving
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}.
This survey provides a comprehensive overview of Vision-Language-Action (VLA) models in autonomous driving, detailing their architecture, evolution, and challenges.
multimodal large language models Vision-Language-Action VLA autonomous driving visual perception natural language understanding control policy explainer reasoning-centric datasets benchmarks driving safety accuracy explanation quality robustness real-time efficiency formal verification interpretable socially aligned