Cover Image

Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Qiqi, Xu, Huan, Li, Jingyu, Sun, Bin, Hao, Zhihui, She, Dangen, Zhu, Xiatian, Zhang, Li
Format:	Preprint
Published:	2026
Subjects:	Robotics Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
by: Li, Yongkang, et al.
Published: (2026)

RealEngine: Simulating Autonomous Driving in Realistic Context
by: Jiang, Junzhe, et al.
Published: (2025)

DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
by: jia, Feiyang, et al.
Published: (2026)

SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
by: Li, Jingyu, et al.
Published: (2026)

Map-World: Masked Action planning and Path-Integral World Model for Autonomous Driving
by: Hu, Bin, et al.
Published: (2025)

DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving
by: Shang, Shuyao, et al.
Published: (2026)

WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving
by: Yang, Pengxuan, et al.
Published: (2025)

DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving
by: Liu, Xiaolu, et al.
Published: (2026)

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

UniDWM: Towards a Unified Driving World Model via Multifaceted Representation Learning
by: Liu, Shuai, et al.
Published: (2026)

A Survey of World Models for Autonomous Driving
by: Feng, Tuo, et al.
Published: (2025)

MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving
by: Huang, Yuzhou, et al.
Published: (2026)

Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
by: Wang, Linbo, et al.
Published: (2026)

AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving
by: Yuan, Zhenlong, et al.
Published: (2025)

ReSim: Reliable World Simulation for Autonomous Driving
by: Yang, Jiazhi, et al.
Published: (2025)

VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
by: Sun, Jingwen, et al.
Published: (2026)

ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving
by: Li, Jingyu, et al.
Published: (2025)

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

Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving
by: Han, Jianhua, et al.
Published: (2025)

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

OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving
by: Wei, Julong, et al.
Published: (2024)

ObjectVLA: End-to-End Open-World Object Manipulation Without Demonstration
by: Zhu, Minjie, et al.
Published: (2025)

HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
by: Li, Qiang, et al.
Published: (2025)

Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning
by: Li, Yue, et al.
Published: (2025)

DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
by: Zhang, Lingjun, et al.
Published: (2026)

Is Your Driving World Model an All-Around Player?
by: Kong, Lingdong, et al.
Published: (2026)

PointVLA: Injecting the 3D World into Vision-Language-Action Models
by: Li, Chengmeng, et al.
Published: (2025)

Galaxea Open-World Dataset and G0 Dual-System VLA Model
by: Jiang, Tao, et al.
Published: (2025)

Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Driving
by: Geng, Jiaheng, et al.
Published: (2025)

HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models
by: Cho, Hoonhee, et al.
Published: (2026)

Beyond Dense Futures: World Models as Structured Planners for Robotic Manipulation
by: Jin, Minghao, et al.
Published: (2026)

KeyWorld: Key Frame Reasoning Enables Effective and Efficient World Models
by: Li, Sibo, et al.
Published: (2025)

SafeAlign-VLA: A Negative-Enhanced Safe Alignment Framework for Risk-Aware Autonomous Driving
by: Tian, Kefei, et al.
Published: (2026)

DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
by: Zhang, Wenyao, et al.
Published: (2025)

DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning
by: Zhou, Yang, et al.
Published: (2026)

Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving
by: Zhang, Dapeng, et al.
Published: (2025)

WAM-Diff: A Masked Diffusion VLA Framework with MoE and Online Reinforcement Learning for Autonomous Driving
by: Xu, Mingwang, et al.
Published: (2025)

UniDrive-WM: Unified Understanding, Planning and Generation World Model For Autonomous Driving
by: Xiong, Zhexiao, et al.
Published: (2026)

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

Self-Correcting VLA: Online Action Refinement via Sparse World Imagination
by: Liu, Chenyv, et al.
Published: (2026)