WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2607.08375v1
• Date: Thu, 09 Jul 2026 11:49:57 GMT
• Title: WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
• Abstract summary: WCog-VLA bridges semantic world forecasting with generative world evolution to achieve proactive autonomous driving.<n>At the semantic level, WCog-VLA unifies world cognition and reasoning by incorporating 3D spatial perception.<n>At the generative level, we introduce the Aligned Decoupled Diffusion Transformer (ADDT) as a powerful generative world model.
• Score: 34.82552727391738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving. However, existing methods either lack comprehensive world cognition or suffer from fragmented world foresight, inherently confining these models to reactive driving. To address this limitation, we propose WCog-VLA, a novel dual-level World-Cognitive VLA framework that successfully bridges semantic world forecasting with generative world evolution to achieve proactive autonomous driving. At the semantic level, WCog-VLA unifies world cognition and reasoning by incorporating 3D spatial perception and injecting agent tokens to capture the world dynamics, while concurrently enabling Game-theoretic Chain-of-Thought (Game-CoT) reasoning. At the generative level, we introduce the Aligned Decoupled Diffusion Transformer (ADDT) as a powerful generative world model that synthesizes physically-plausible joint multi-agent trajectories. Through scene representation alignment, ADDT reduces the number of denoising steps required and thus significantly accelerates inference. To facilitate strategic reasoning, we further construct a large-scale dataset featuring 85k Game-CoT annotations. Extensive experiments on the NAVSIM benchmark demonstrate that WCog-VLA achieves a State-Of-The-Art (SOTA) PDMS score of 92.9.

Related papers

• CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving [4.4380564455353975]
  CoWorld-VLA is a multi-expert world reasoning framework for autonomous driving.<n>World representations serve as explicit conditions to guide action planning.<n>Experiments show that CoWorld-VLA achieves competitive results in both future scene generation and planning.
  arXiv  Detail & Related papers  (2026-05-11T12:01:13Z)
• OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation [61.18260993245354]
  Chain-of-Thought (CoT) reasoning is a powerful driver of trajectory prediction in VLA-based autonomous driving.<n>We present OneVL, a unified VLA and World Model framework that routes reasoning through compact latent tokens supervised by dual auxiliary decoders.<n>OneVL becomes the first latent CoT method to surpass explicit CoT, delivering state-of-the-art accuracy at answer-only latency.
  arXiv  Detail & Related papers  (2026-04-20T16:37:22Z)
• UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving [48.461267171124945]
  We propose UniDriveVLA, a Unified Driving Vision-Language-Action model for autonomous driving.<n>It comprises three experts for driving understanding, scene perception, and action planning, which are coordinated through masked joint attention.<n>It achieves state-of-the-art performance in open-loop evaluation on nuScenes and closed-loop evaluation on Bench2Drive.
  arXiv  Detail & Related papers  (2026-04-02T15:48:45Z)
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving [25.94896726674773]
  DriveWorld-VLA is a novel framework that unifies world modeling and planning within a latent space.<n>It supports controllable, action-conditioned imagination at the feature level.<n>It achieves state-of-the-art performance with 91.3 PDMS on NAVSIMv1, 86.8S on NAVSIMv2, and 0.16 3-second average collision rate on nuScenes.
  arXiv  Detail & Related papers  (2026-02-06T09:25:48Z)
• dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning [69.36145467833498]
  We introduce dVLM-AD, a diffusion-based vision-language model that unifies perception, structured reasoning, and low-level planning for end-to-end driving.<n> evaluated on nuScenes and WOD-E2E, dVLM-AD yields more consistent reasoning-action pairs and achieves planning performance comparable to existing driving VLM/VLA systems.
  arXiv  Detail & Related papers  (2025-12-04T05:05:41Z)
• dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought [66.78110237549087]
  Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics.<n>We introduce dVLA, a diffusion-based VLA that unifies visual perception, language reasoning, and robotic control in a single system.
  arXiv  Detail & Related papers  (2025-09-30T02:36:11Z)
• AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning [37.176428069948535]
  Vision-Language-Action (VLA) models have shown promise for end-to-end autonomous driving.<n>Current VLA models struggle with physically infeasible action outputs, complex model structures, or unnecessarily long reasoning.<n>We propose AutoVLA, a novel VLA model that unifies reasoning and action generation within a single autoregressive generation model.
  arXiv  Detail & Related papers  (2025-06-16T17:58:50Z)
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving [49.07731497951963]
  ReCogDrive is a novel Reinforced Cognitive framework for end-to-end autonomous driving.<n>We introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers.<n>We then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner.
  arXiv  Detail & Related papers  (2025-06-09T03:14:04Z)
• OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning [68.45848423501927]
  We propose a holistic vision-language dataset that aligns agent models with 3D driving tasks through counterfactual reasoning.<n>Our approach enhances decision-making by evaluating potential scenarios and their outcomes, similar to human drivers considering alternative actions.
  arXiv  Detail & Related papers  (2025-04-06T03:54:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.