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

2025-02-18

AI Summary

Existing saliency models (e.g., TASED-Net) exhibit substantial performance degradation—over 12% AUC drop—in predicting gaze behavior of two-wheeler drivers under India’s complex, unstructured traffic conditions, revealing critical limitations in cross-regional generalization. To address this, we introduce the first naturalistic eye-tracking dataset specifically designed for two-wheeler drivers in real Indian road environments, featuring synchronized GPS, IMU, and video-based annotations. Through systematic analysis, we characterize distinct visual attention patterns and decision-making mechanisms that differentiate two-wheeler from four-wheeler drivers. Cross-domain evaluation confirms the failure of mainstream saliency models on this setting, motivating context-specific saliency modeling. This dataset fills a critical gap in localized driving behavior research and provides foundational support for enhancing two-wheeler road safety and developing cost-effective intelligent driver assistance systems.

Technology Category

• Computer Vision: Motion & Tracking
• Application Domains: Mobility, Driving & Flight
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Unique gaze behaviour of Indian two-wheeler drivers
• Inadequacy of existing saliency models in Indian traffic
• Need for context-specific models for road safety

Innovation

• Real-world eye-tracking dataset
• Context-specific saliency models
• Two-wheeler driver behavior analysis