Cover Image

DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fu, Yongjie, Jain, Anmol, Di, Xuan, Chen, Xu, Mo, Zhaobin
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model
by: Fu, Yongjie, et al.
Published: (2024)

DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
by: Tian, Xiaoyu, et al.
Published: (2024)

SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets
by: Mo, Zhaobin, et al.
Published: (2025)

SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
by: Li, Peizheng, et al.
Published: (2025)

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

VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision
by: Xu, Yi, et al.
Published: (2024)

DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving
by: Zhou, Yang, et al.
Published: (2026)

DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models
by: Zhou, Xirui, et al.
Published: (2025)

GenAD: Generalized Predictive Model for Autonomous Driving
by: Yang, Jiazhi, et al.
Published: (2024)

Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models
by: Wang, Jiahao, et al.
Published: (2025)

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

Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving
by: Chen, Xuyang, et al.
Published: (2026)

ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models
by: Sural, Shounak, et al.
Published: (2024)

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving
by: Zhang, Tianyuan, et al.
Published: (2024)

GenAD: Generative End-to-End Autonomous Driving
by: Zheng, Wenzhao, et al.
Published: (2024)

DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking
by: Zheng, Weicheng, et al.
Published: (2025)

DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving
by: Shi, Chen, et al.
Published: (2026)

Spatial-aware Vision Language Model for Autonomous Driving
by: Wei, Weijie, et al.
Published: (2025)

LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement
by: Jiao, Siwen, et al.
Published: (2024)

FAR-Drive: Frame-AutoRegressive Video Generation in Closed-Loop Autonomous Driving
by: Li, Yaoru, et al.
Published: (2026)

DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving
by: Diao, Muxi, et al.
Published: (2025)

OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving
by: Tang, Tao, et al.
Published: (2025)

CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving
by: Ji, Yishen, et al.
Published: (2025)

MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning
by: Fu, Haoyu, et al.
Published: (2025)

SimGen: Simulator-conditioned Driving Scene Generation
by: Zhou, Yunsong, et al.
Published: (2024)

Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving
by: Zhao, Zongchuang, et al.
Published: (2025)

VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving
by: Huang, Zilin, et al.
Published: (2024)

DriveCtrl: Conditioned Sim-to-Real Driving Video Generation
by: Zhao, Haonan, et al.
Published: (2026)

VLP: Vision Language Planning for Autonomous Driving
by: Pan, Chenbin, et al.
Published: (2024)

dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning
by: Ma, Yingzi, et al.
Published: (2025)

VERDI: VLM-Embedded Reasoning for Autonomous Driving
by: Feng, Bowen, et al.
Published: (2025)

Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving
by: Xiong, Minhao, et al.
Published: (2025)

OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
by: Zhou, Xingcheng, et al.
Published: (2025)

Judge, Then Drive: A Critic-Centric Vision Language Action Framework for Autonomous Driving
by: Yang, Lijin, et al.
Published: (2026)

OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
by: Wang, Shihao, et al.
Published: (2025)

OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
by: Wang, Shihao, et al.
Published: (2024)

DriveGen3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion
by: Wang, Weijie, et al.
Published: (2025)

MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control
by: Gao, Ruiyuan, et al.
Published: (2024)

Language Prompt for Autonomous Driving
by: Wu, Dongming, et al.
Published: (2023)

Comparative Analysis of Patch Attack on VLM-Based Autonomous Driving Architectures
by: Fernandez, David, et al.
Published: (2026)