2025/08/07 by Abdul Hamid K, Hamid, Kaiser, Khandakar Ashrafi Akbar +3
Psychology · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human-Automation Interaction and Safety #I.5.4 #Safety Warnings and Signage
paper · pdf · doi:10.48550/arxiv.2508.05852
openalex publication_date 2025/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which road object or region is being monitored or why an attention shift may matter. This study examines whether minimal human-grounded supervision can steer a vision--language model toward interpretable descriptions of driver attention shifts. Using selected high-change gaze moments from the Berkeley DeepDrive-Attention dataset, we compare zero-shot, one-shot, and LoRA fine-tuned VLM conditions against human-refined reference descriptions and expert ratings. Results show that fine-tuning with 80 expert-refined attention examples improves ROUGE-L, METEOR, Entity Alignment F1, and Human Alignment Score relative to unsteered VLM outputs. The findings suggest that language-based descriptions can complement gaze heatmaps by making driver attention more accessible for human-factors analysis, driver-monitoring review, and situation-awareness support.