SURVEY ON DETECTION OF DRIVER DISTRACTION AND CLASSIFICATION USING MACHINE LEARNING

Mr. Varad Bagal; Dr. Sangram T. Patil; Dr. Jaydeep B. Patil

SURVEY ON DETECTION OF DRIVER DISTRACTION AND CLASSIFICATION USING MACHINE LEARNING

Authors: Mr. Varad Bagal, Dr. Sangram T. Patil, Dr. Jaydeep B. Patil

Unique Paper ID: 168118
Volume: 11
Issue: 4
PageNo: 1508-1513

Keywords: cognitive distraction eye tracking vehicle kinematics data etc

Abstract:
Distracted driving is one of the main reasons for collisions. Therefore, it is crucial to constantly monitor the driving condition of drivers and offer suitable solutions to those who are distracted. When a driver's eyes are fixed on the road ahead but their mind is elsewhere, it's referred to as cognitive distraction. Cognitive distractions typically come from being tired, talking to a fellow passenger, listening to the radio, or engaging in other mentally taxing side activities that don't need a driver to take their eyes off the road. Because there are no outward signs of driver distraction, it is one of the most difficult diversions to identify. Authors have identified features from various sources, including eye tracking, physiological, and vehicle kinematics data, that are relevant towards the classification of distracted and non-distracted drivers in this study. The three main classification techniques that were used are Support Vector Machines, Decision Trees, and Random Forest. It was discovered that a condensed feature set consisting of pupil area, pupil vertical motion, and pupil horizontal motion could predict driver distraction with an average accuracy on a variety of road conditions. Additionally, the impact that various road types had on drivers' behaviour was discovered. The study's conclusions can be applied practically to the development of technologies that monitor driver distraction.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 4
PageNo: 1508-1513

SURVEY ON DETECTION OF DRIVER DISTRACTION AND CLASSIFICATION USING MACHINE LEARNING

Available:https://ijirt.org/Article?manuscript=168118

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UGC Approved
Journal no 47859

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