首个印度两轮车司机真实路况眼动数据集，揭示独特视觉行为模式。
myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset
- 采集印度复杂路况下两轮车司机的真实眼动数据，填补研究空白。
- 现有注意力模型在本数据集上表现显著下降，说明需定制化建模。
- 适合交通安全、驾驶行为研究者，尤其关注新兴市场交通场景。
本文提出 myEye2Wheeler 数据集，是首个针对印度复杂交通环境下两轮车司机真实路况下的眼动行为数据资源。多数现有数据集来自四轮车司机在规划良好的道路和同质化交通中的观测，而本数据集揭示了印度两轮车司机独特的视觉注意力模式与决策机制。分析显示，如 TASED-Net 等现有显著性模型在 myEye2Wheeler 数据集上的表现远低于其在欧洲四轮车眼动数据集 DR(Eye)VE 上的表现，凸显了为特定交通环境定制注意力模型的必要性。该数据集不仅弥补了印度两轮车驾驶行为研究的空白，更旨在提升两轮车用户道路安全，并支持经济高效的交通方式规划。
原文摘要 · Abstract (English)
This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homogeneous traffic. Our dataset offers a critical lens into the unique visual attention patterns and insights into the decision-making of Indian two-wheeler drivers. The analysis demonstrates that existing saliency models, like TASED-Net, perform less effectively on the myEye-2Wheeler dataset compared to when applied on the European 4-wheeler eye tracking datasets (DR(Eye)VE), highlighting the need for models specifically tailored to the traffic conditions. By introducing the dataset, we not only fill a significant gap in two-wheeler driver behaviour research in India but also emphasise the critical need for developing context-specific saliency models. The larger aim is to improve road safety for two-wheeler users and lane-planning to support a cost-effective mode of transport.
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