Gaze Fixation System for the Evaluation of Driver Distractions Induced by IVIS

Gil-Jiménez, Pedro; Bergasa, Luis M.; Nuevo, J.; Hernández, Noelia; Daza, Iván García · 2012 · OpenAlex-citations

DOI: 10.1109/tits.2012.2187517

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Summary

This paper presents a non-intrusive, vision-based system for monitoring driver distraction caused by In-Vehicle Information Systems (IVIS). Motivated by the significant safety risks associated with driving inattention, particularly the increasing use of devices like GPS, hands-free phones, and on-board computers, the authors aim to provide an objective method for assessing distraction patterns. Unlike previous approaches that rely on intrusive biological sensors or indirect vehicle signals, this system uses a stereo camera to estimate face pose and gaze direction in real-time, allowing for the classification of where a driver is looking without requiring subject-specific calibration or self-reported data. The methodology employs a calibrated stereo rig equipped with infrared illumination to handle low-light conditions and enhance eye detection via the bright pupil effect. The system first creates a sparse 3D model of the driver’s face using Harris interest points and tracks these features across frames to estimate head pose over a full yaw rotation range (-90° to +90°). Eye direction is then calculated by localizing the pupil center using integral projections and combining this vector with the estimated face pose to determine the gaze fixation point. This fixation point is classified into one of 11 predefined areas of interest, such as the road, lateral mirrors, or specific IVIS locations, using Mahalanobis distance. The system was validated in a naturalistic driving simulator equipped with a real truck cabin and various IVIS. Twelve professional drivers participated in 16 exercises across four scenarios (mountain, inter-city, urban, and long-distance), which included control runs and tasks designed to induce visual, auditory, and cognitive distractions. Ground truth for face pose was established using a helmet-mounted calibration chessboard, while gaze fixation labels were manually annotated from video recordings. The system demonstrated robust performance, operating effectively despite fast head movements and varying lighting, and provided consistent statistics on driver attention distribution. The significance of this work lies in its ability to automatically generate objective distraction metrics without user intervention, addressing a gap in existing research that often relies on subjective reports or limited environmental controls. By distinguishing between different fixation areas, the system enables psychologists and engineers to analyze how specific IVIS tasks impact driver attention. The authors conclude that this approach offers a cost-efficient, reliable tool for studying driver behavior in realistic settings, potentially supporting the development of adaptive IVIS that mitigate distraction risks.

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Topics

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• gaze based attention detection
• visual
• distraction detection algorithms
• attention allocation
• dms validation
• eye movements scanning

Information type

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• Methodological Resource: tool software, measurement protocol, validation psychometrics