LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving

• URL: http://arxiv.org/abs/2606.29879v2
• Date: Tue, 30 Jun 2026 16:50:58 GMT
• Title: LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
• Abstract summary: We develop the Layer-Wise World-Model-Guided Driving framework (LWDrive)<n>LWDrive is a VLM planning framework that refines coarse trajectories through layer-wise world-model guidance.<n> Experiments show that LWDrive achieves a score of 92.0 on the NAVSIM benchmark and 89.6 on NAVSIM-v2.
• Score: 13.964957969409655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning. However, trajectories directly generated by VLMs often encode only coarse driving intentions and remain insufficient for geometrically accurate, future-aware, and multi-view-grounded planning. To address these limitations, we develop the Layer-Wise World-Model-Guided Driving framework (LWDrive). LWDrive is a VLM planning framework that refines coarse trajectories through layer-wise world-model guidance. Instead of treating the VLM output as the final trajectory, LWDrive uses it as an intent-aware coarse plan, expands a diverse candidate space around it, and progressively refines the candidates through a Foresight Cascade Planner (FCP). Specifically, we introduce future-frame generation supervision to encourage the VLM to learn forward-looking scene representations, thereby injecting planning-relevant predictive dynamics into its internal hidden states. Built upon these world-model-supervised representations, FCP exploits VLM features across multiple layers and integrates historical temporal states, Action-Query representations, and current-frame multi-view Bird's-Eye-View (BEV) features to refine candidate trajectories in a coarse-to-fine manner. This design enables progressive correction of spatial positions and motion trends while grounding trajectory refinement with multi-view scene cues and preserving the high-level driving intention produced by the large model. Finally, a score head evaluates the refined candidates and selects the best trajectory as the final planning output. Experiments show that LWDrive achieves a score of 92.0 on the NAVSIM benchmark and 89.6 on NAVSIM-v2. Code and models will be made publicly available.

Related papers

• LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model [16.87741001074065]
  Vision-Language-Action (VLA) models have emerged as a promising framework for end-to-end autonomous driving.<n>Recent attempts to incorporate dense visual supervision via world modeling often overemphasize pixel-level image reconstruction.<n>We propose LVDrive, a Latent Visual representation enhanced VLA framework for autonomous driving.
  arXiv  Detail & Related papers  (2026-05-21T07:31:49Z)
• LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving [60.31765454895336]
  We present LMGenDrive, the first framework that combines multimodal understanding with generative world models for end-to-end closed-loop driving.<n>We propose a progressive three-stage training strategy, from vision pretraining to multi-step long-horizon driving, to improve stability and performance.
  arXiv  Detail & Related papers  (2026-04-09T19:13:14Z)
• DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA [72.9197085473598]
  DIAL is a framework bridging high-level decision making and low-level motor execution through a differentiable latent intent bottleneck.<n>A VLM-based System-2 performs latent world modeling by latent visual foresight within the VLM's native feature space.<n>A lightweight System-1 policy then decodes this predicted intent together with the current observation into precise robot actions.
  arXiv  Detail & Related papers  (2026-03-31T15:02:27Z)
• Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving [52.04950569530877]
  We present Uni-World VLA, a unified vision-language-action model that tightly interleaves future frame prediction and trajectory planning.<n>Our approach achieves competitive closed-loop planning performance while producing high-fidelity future frame predictions.
  arXiv  Detail & Related papers  (2026-03-28T14:39:51Z)
• Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation [66.7879424097418]
  We present WorldDrive, a holistic framework that couples scene generation and real-time planning via unifying vision and motion representation.<n>A simple interaction between motion representation, visual representation, and ego status can generate high-quality, multi-modal trajectories.<n>Experiments on the NAVSIM, NAVSIM-v2, and nuScenes benchmarks demonstrate that WorldDrive achieves leading planning performance among vision-only methods.
  arXiv  Detail & Related papers  (2026-03-16T07:59:39Z)
• NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving [4.400011068855375]
  We propose NaviDriveVLM, a framework that separates reasoning from action generation using a large-scale Navigator and a lightweight trainable Driver.<n> Experiments on the nuScenes benchmark show that NaviDriveVLM outperforms large VLM baselines in end-to-end motion planning.
  arXiv  Detail & Related papers  (2026-03-09T02:47:44Z)
• AppleVLM: End-to-end Autonomous Driving with Advanced Perception and Planning-Enhanced Vision-Language Models [11.748457186467727]
  We propose AppleVLM, an advanced perception and planning-enhanced VLM model for robust end-to-end driving.<n>AppleVLM introduces a novel vision encoder and a planning strategy encoder to improve perception and decision-making.<n>We evaluate AppleVLM in closed-loop experiments on two CARLA benchmarks, achieving state-of-the-art driving performance.
  arXiv  Detail & Related papers  (2026-02-04T06:37:14Z)
• SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving [52.02379432801349]
  We propose SGDrive, a novel framework that structures the VLM's representation learning around driving-specific knowledge hierarchies.<n>Built upon a pre-trained VLM backbone, SGDrive decomposes driving understanding into a scene-agent-goal hierarchy that mirrors human driving cognition.
  arXiv  Detail & Related papers  (2026-01-09T08:55:42Z)
• UniDrive-WM: Unified Understanding, Planning and Generation World Model For Autonomous Driving [29.623672055601418]
  We propose a unified VLM-based world model that jointly performs driving-scene understanding, trajectory planning, and trajectory-conditioned future image generation.<n>Experiments on the Bench2Drive benchmark show that UniDrive-WM produces high-fidelity future images and improves planning performance by 5.9% in L2 trajectory error and 9.2% in collision rate over the previous best method.
  arXiv  Detail & Related papers  (2026-01-07T23:49:52Z)
• Enhancing End-to-End Autonomous Driving with Latent World Model [78.22157677787239]
  We propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving.<n> LAW predicts future scene features based on current features and ego trajectories.<n>This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks.
  arXiv  Detail & Related papers  (2024-06-12T17:59:21Z)

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.