AI RESEARCH
Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
arXiv CS.AI
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ArXi:2512.03454v3 Announce Type: replace-cross Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) methods for autonomous vehicles (AVs) typically struggle with ambiguous, context-dependent instructions, as they lack reasoning over 3D spatial relations and anticipated scene evolution. Grounded in the principles of world models, we propose ThinkDeeper, a framework that reasons about future spatial states before making grounding decisions.