A Survey on Vision-Language-Action Models for Autonomous Driving
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912458234396672 |
|---|---|
| author | Jiang, Sicong Huang, Zilin Qian, Kangan Luo, Ziang Zhu, Tianze Zhong, Yang Tang, Yihong Kong, Menglin Wang, Yunlong Jiao, Siwen Ye, Hao Sheng, Zihao Zhao, Xin Wen, Tuopu Fu, Zheng Chen, Sikai Jiang, Kun Yang, Diange Choi, Seongjin Sun, Lijun |
| author_facet | Jiang, Sicong Huang, Zilin Qian, Kangan Luo, Ziang Zhu, Tianze Zhong, Yang Tang, Yihong Kong, Menglin Wang, Yunlong Jiao, Siwen Ye, Hao Sheng, Zihao Zhao, Xin Wen, Tuopu Fu, Zheng Chen, Sikai Jiang, Kun Yang, Diange Choi, Seongjin Sun, Lijun |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_24044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Vision-Language-Action Models for Autonomous Driving Jiang, Sicong Huang, Zilin Qian, Kangan Luo, Ziang Zhu, Tianze Zhong, Yang Tang, Yihong Kong, Menglin Wang, Yunlong Jiao, Siwen Ye, Hao Sheng, Zihao Zhao, Xin Wen, Tuopu Fu, Zheng Chen, Sikai Jiang, Kun Yang, Diange Choi, Seongjin Sun, Lijun Computer Vision and Pattern Recognition Artificial Intelligence Robotics 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}. |
| title | A Survey on Vision-Language-Action Models for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2506.24044 |