UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving

UniDrive is a novel unified vision-language and grounding framework designed for interpretable risk understanding in autonomous driving, addressing the limitations of existing multimodal large language models (MLLMs) in temporal reasoning and spatial precision. The architecture features a dual-branch system: a temporal reasoning branch for multi-frame scene dynamics and a high-resolution perception branch for fine-grained spatial details, integrated via a gated cross-attention fusion module. Benchmark results on the DRAMA-Reasoning dataset indicate that UniDrive surpasses image-based and video-based baselines in risk-object localization and interpretability, highlighting its potential for enhancing safety in autonomous driving systems.