Region-Level Vision-Language Model for Detecting Distraction Behavior and Mobility Attributes of Vulnerable Road Users

Vulnerable road users (VRUs) such as pedestrians and cyclists frequently engage in distracted behaviors (e.g., phone or headphone use) that elevate crash risk. General-purpose vision–language models (VLMs) struggle to capture these subtle, small, and context-dependent cues, and most camera pipelines still rely on frame-based analyses that ignore temporal context. We present IntersectionPAR, a lightweight vision–language framework that couples an Interactive Attribute Encoder with region-level captioning to recover safety-relevant semantic attributes (gender, phone/headphone use, mobility mode and aids) together with kinematic cues in a bird’s-eye view. We further introduce a temporal risk module that computes a continuous Situational Awareness Score (from phone, pose, and gaze proxies) and a Dynamic Risk Score that fuses awareness with spatial context (safe vs. unsafe zones), aligning system output with the specific moments practitioners need to act. Our dataset contains 12,273 annotated images drawn from~200 intersection-crossing scenarios under varied lighting and weather. Under a standardized zero-shot protocol against a broad suite of modern VLMs, IntersectionPAR attains keyword-based accuracy of 0.6123 with balanced precision/recall (0.7936/0.7864) and an F1-score of 0.7719 while requiring only ~9 GB of VRAM(>40 GB for several baselines). Qualitative analyses show strong behavior on safety-critical cues (e.g., cane and mobility-aid detection) and illustrate how the temporal risk module elevates risk precisely when low-awareness VRUs enter the roadway. Statistical analyses of 200 crossing events further reveal behavioral impacts of phone use (e.g., longer waiting time and increased path deviation). By pairing accurate, attribute-aware perception with low computational cost and an actionable temporal risk signal, IntersectionPAR supports real-time intersection safety monitoring and proactive interventions to mitigate VRU risk.