myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset
This addresses a gap in two-wheeler driver behavior research in India, with incremental impact on road safety and lane-planning for this mode of transport.
The paper tackles the lack of real-world gaze data for two-wheeler drivers in complex Indian traffic by introducing the myEye2Wheeler dataset, showing that existing saliency models like TASED-Net perform less effectively on this dataset compared to European four-wheeler datasets.
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.