LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
arXiv:2606.29879v1 Announce Type: cross 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 guid
The rapid advancement of Vision-Language Models (VLMs) and the increasing demand for robust autonomous driving solutions are converging to push the boundaries of AI planning in real-world applications.
This development represents a significant step towards more reliable and sophisticated autonomous driving systems, potentially accelerating the deployment and adoption of self-driving technology by addressing critical safety and accuracy limitations.
Autonomous driving systems can now incorporate more nuanced, geometrically accurate, and contextually aware planning by refining VLM outputs with world-model guidance, moving beyond coarse trajectory generation.
- · Autonomous vehicle manufacturers
- · AI software developers
- · Logistics companies
- · Consumers of autonomous services
- · Traditional human-driven transport services
- · Companies with less sophisticated AI planning capabilities
Improved safety and efficiency of autonomous vehicles become achievable with more precise VLM-guided planning.
Faster regulatory approval and public acceptance of autonomous driving technology may follow enhanced reliability.
The development of highly capable autonomous systems could lead to a re-architecting of urban planning and supply chain logistics.
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Read at arXiv cs.AI