# Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction

arXiv: https://arxiv.org/abs/2605.24040 · HTML: https://mlsanity.com/paper/2605.24040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 17 of 17)

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- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
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- [Tell Me Where to Look: Guided Attention Inference Network](https://mlsanity.com/paper/1802.10171/) (2018)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning](https://mlsanity.com/paper/2205.12466/) (2022)
- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [FARSA: Fully Automated Roadway Safety Assessment](https://mlsanity.com/paper/1901.06013/) (2019)
- [CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning](https://mlsanity.com/paper/2102.00565/) (2021)
- [Gaze-Informed Vision Transformers: Predicting Driving Decisions Under Uncertainty](https://mlsanity.com/paper/2308.13969/) (2023)
- [Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons](https://mlsanity.com/paper/2412.09835/) (2024)
- [AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems](https://mlsanity.com/paper/2504.05950/) (2025)

## Abstract (arXiv)

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.