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The two branches are integrated through a gated cross-attention fusion module, enabling dynamic context to be aligned with precise spatia","title":"UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving","url":"https://arxiv.org/abs/2606.24759","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.24759v1 Announce Type: cross \nAbstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision. Models that rely on single-frame or low-resolution inputs often miss small, distant, or partially occluded hazards, while language-centric driving models frequently provide limited grounded evidence for their explanations. To address this gap, we propose UniDrive, a unified visual-language and grounding framework for interpretable risk understanding in autonomous driving. 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