Buchumschlag

Learning Vision-Language-Action World Models for Autonomous Driving

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Wang, Guoqing, Tang, Pin, Ren, Xiangxuan, Zhao, Guodongfang, Feng, Bailan, Ma, Chao
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Computer Vision and Pattern Recognition Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving
von: Wang, Guoqing, et al.
Veröffentlicht: (2024)

SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction
von: Tang, Pin, et al.
Veröffentlicht: (2024)

VEON: Vocabulary-Enhanced Occupancy Prediction
von: Zheng, Jilai, et al.
Veröffentlicht: (2024)

Grounding Everything in Tokens for Multimodal Large Language Models
von: Ren, Xiangxuan, et al.
Veröffentlicht: (2025)

LiteFusion: Taming 3D Object Detectors from Vision-Based to Multi-Modal with Minimal Adaptation
von: Ren, Xiangxuan, et al.
Veröffentlicht: (2025)

A Survey on Vision-Language-Action Models for Autonomous Driving
von: Jiang, Sicong, et al.
Veröffentlicht: (2025)

VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving
von: Zhao, Rui, et al.
Veröffentlicht: (2026)

RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving
von: Wang, Yujin, et al.
Veröffentlicht: (2025)

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving
von: Tang, Yihong, et al.
Veröffentlicht: (2025)

LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model
von: Mei, Xiaodong, et al.
Veröffentlicht: (2026)

TurboVSR: Fantastic Video Upscalers and Where to Find Them
von: Wang, Zhongdao, et al.
Veröffentlicht: (2025)

DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
von: Yang, Zhenjie, et al.
Veröffentlicht: (2025)

DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale
von: Zuo, Sicheng, et al.
Veröffentlicht: (2026)

Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving
von: Liu, Ming, et al.
Veröffentlicht: (2025)

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving
von: Wang, Lu, et al.
Veröffentlicht: (2025)

Vision Language Models in Autonomous Driving: A Survey and Outlook
von: Zhou, Xingcheng, et al.
Veröffentlicht: (2023)

HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Driving
von: Wang, Yiru, et al.
Veröffentlicht: (2026)

DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
von: Li, Yingyan, et al.
Veröffentlicht: (2025)

A Unified Perception-Language-Action Framework for Adaptive Autonomous Driving
von: Zhang, Yi, et al.
Veröffentlicht: (2025)

Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving
von: Zhang, Enming, et al.
Veröffentlicht: (2025)

VLM-AutoDrive: Post-Training Vision-Language Models for Safety-Critical Autonomous Driving Events
von: Bhat, Mohammad Qazim, et al.
Veröffentlicht: (2026)

ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
von: Tang, Zuojin, et al.
Veröffentlicht: (2026)

OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving
von: Wang, Lening, et al.
Veröffentlicht: (2024)

Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving
von: Jiang, Hao, et al.
Veröffentlicht: (2025)

VL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving
von: Xu, Zhefan, et al.
Veröffentlicht: (2026)

DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving
von: Fu, Yongjie, et al.
Veröffentlicht: (2024)

DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
von: Huang, Zilin, et al.
Veröffentlicht: (2026)

Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
von: Liao, Haicheng, et al.
Veröffentlicht: (2025)

Less is More: Lean yet Powerful Vision-Language Model for Autonomous Driving
von: Yang, Sheng, et al.
Veröffentlicht: (2025)

IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model
von: Jiang, Anqing, et al.
Veröffentlicht: (2025)

RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models
von: Wang, Yujin, et al.
Veröffentlicht: (2024)

DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
von: Jiang, Anqing, et al.
Veröffentlicht: (2025)

ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving
von: Sha, Lin, et al.
Veröffentlicht: (2026)

CoT4AD: A Vision-Language-Action Model with Explicit Chain-of-Thought Reasoning for Autonomous Driving
von: Wang, Zhaohui, et al.
Veröffentlicht: (2025)

AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving
von: Huang, Lianming, et al.
Veröffentlicht: (2025)

Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving
von: Gopalkrishnan, Akshay, et al.
Veröffentlicht: (2024)

Does Visual Information Play a Decisive Role in Vision-Language-Action Model Driving Behavior?
von: He, Jingtao, et al.
Veröffentlicht: (2026)

CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
von: Huang, Minqing, et al.
Veröffentlicht: (2026)

SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving
von: Zheng, Peiru, et al.
Veröffentlicht: (2024)

VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
von: Zhao, Rui, et al.
Veröffentlicht: (2026)