iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning

Manyi Yao, Bingbing Zhuang, Sparsh Garg, Amit Roy-Chowdhury, Christian R. Shelton, Manmohan Chandraker, Abhishek Aich

Abstract:

Grounding large language models (LLMs) in domain-specific tasks like post-hoc
dash-cam driving video analysis is challenging due to their general-purpose training and lack of structured inductive biases. As vision is often the sole modality available for such analysis (i.e. no LiDAR, GPS, etc.), existing video-based
vision-language models (V-VLMs) struggle with spatial reasoning, causal inference, and explainability of events in the input video. To this end, we introduce iFinder, a structured semantic grounding framework that decouples perception
from reasoning by translating dash-cam videos into a hierarchical, interpretable
data structure for LLMs. iFinder operates as a modular, training-free pipeline
that employs pretrained vision models to extract critical cues—object pose, lane
positions, and object trajectories—which are hierarchically organized into frame and video-level structures. Combined with a three-block prompting strategy, it enables step-wise, grounded reasoning for the LLM to refine a peer V-VLM’s
outputs and provide accurate reasoning. Evaluations on four public dash-cam
video benchmarks show that iFinder’s proposed grounding with domain-specific
cues—especially object orientation and global context—significantly outperforms
end-to-end V-VLMs on four zero-shot driving benchmarks, with up to 39% gains
in accident reasoning accuracy. By grounding LLMs with driving domain-specific
representations, iFinder offers a zero-shot, interpretable, and reliable alternative
to end-to-end V-VLMs for post-hoc driving video understanding.