myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset

TL;DR

This paper introduces myEye2Wheeler, a novel eye-tracking dataset capturing the visual attention of Indian two-wheeler drivers in real-world traffic, highlighting the need for specialized saliency models for such contexts.

Contribution

The creation of the first real-world eye-tracking dataset for Indian two-wheeler drivers, revealing unique attention patterns and the limitations of existing models in this domain.

Findings

Existing saliency models perform poorly on the dataset.

Two-wheeler drivers exhibit distinct visual attention patterns.

Highlights the need for context-specific saliency models.

Abstract

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.