UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving
arXiv:2606.24759v1 Announce Type: cross Abstract: 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 inter
The rapid advancements in large language models, particularly multimodal variants, are enabling new approaches to complex real-world problems like autonomous driving interpretation.
Improved interpretability and risk understanding in autonomous driving systems are critical for public acceptance, regulatory approval, and addressing intrinsic safety challenges, moving autonomous vehicles closer to widespread deployment.
Current autonomous driving systems often lack clear, grounded explanations for their decisions; UniDrive's approach introduces a framework addressing this, offering more transparent and safer operation possibilities.
- · Autonomous vehicle developers
- · AI safety researchers
- · Automotive industry
- · Insurance providers
- · Companies relying on opaque AI systems
- · Traditional risk assessment models
Enhances the safety and trustworthiness of Level 4/5 autonomous driving systems.
Accelerates the regulatory approval and public adoption rates of autonomous vehicles.
Potentially reduces accident rates significantly and reshapes urban mobility paradigms.
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Read at arXiv cs.AI