{"slug": "learning-to-see-like-humans-gaze-aligned-cycling-safety-prediction", "title": "Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction", "summary": "Researchers have developed a gaze-aligned artificial intelligence framework that predicts perceived cycling safety by integrating human eye-tracking data into image analysis. The model, called EG-PCS, uses vision transformers supervised with eye-tracking signals to produce attention maps that more accurately reflect how humans visually assess street safety. The approach enhances both predictive accuracy and interpretability in urban analytics, addressing a key barrier to cycling adoption in cities.", "body_md": "arXiv:2605.24040v1 Announce Type: new\nAbstract: Cycling delivers significant public-health and environmental benefits, yet its uptake in cities is often limited by perceived safety. When street environments appear unsafe, individuals are less likely to cycle, making perception a key barrier to adoption. Recent work has shown that pairwise comparisons of street-view images provide a scalable way to learn subjective safety judgments. However, existing approaches do not explicitly model human visual attention, which plays a central role in how humans perceive safety. We propose an Eye-Tracking-Guided Perceived Cycling Safety framework (EG-PCS) that integrates gaze data into a pairwise learning pipeline based on vision transformers. By supervising the model's attention mechanism with eye-tracking signals, we encourage alignment between learned attention maps and human fixation patterns. Experiments show that gaze-guided models achieve similar ranking performance compared to state-of-the-art approaches while producing attention maps that more accurately reflect human visual attention behavior. Our results demonstrate that incorporating eye-tracking information enhances both predictive accuracy and interpretability in perception-based urban analytics.", "url": "https://wpnews.pro/news/learning-to-see-like-humans-gaze-aligned-cycling-safety-prediction", "canonical_source": "https://arxiv.org/abs/2605.24040", "published_at": "2026-05-26 04:00:00+00:00", "updated_at": "2026-05-26 04:12:08.887438+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "artificial-intelligence", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/learning-to-see-like-humans-gaze-aligned-cycling-safety-prediction", "markdown": "https://wpnews.pro/news/learning-to-see-like-humans-gaze-aligned-cycling-safety-prediction.md", "text": "https://wpnews.pro/news/learning-to-see-like-humans-gaze-aligned-cycling-safety-prediction.txt", "jsonld": "https://wpnews.pro/news/learning-to-see-like-humans-gaze-aligned-cycling-safety-prediction.jsonld"}}