Enhancing Road Safety: Detecting Texting Distracted Driving with Eye-Tracking and Machine Learning
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© 2023 by IJCTT Journal
Volume-71 Issue-10
Year of Publication : 2023
Authors : Rohan Singh Rajput, Shantanu Neema
DOI :  10.14445/22312803/IJCTT-V71I10P112

How to Cite?

Rohan Singh Rajput, Shantanu Neema, "Enhancing Road Safety: Detecting Texting Distracted Driving with Eye-Tracking and Machine Learning ," International Journal of Computer Trends and Technology, vol. 71, no. 10, pp. 107-113, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I10P112

Abstract
Driving while texting is risky as it diverts the driver's attention from the road and requires them to shift between handling their phone and the vehicle. Despite its dangers, many still engage in this behaviour. To address this, some companies have implemented features to detect when someone is driving and restrict calls and messages unless confirmed otherwise by the user. This research introduces a method that combines eye-tracking technology with machine learning to identify when a driver is texting. A driving simulator was utilized to evaluate 26 participants under various conditions: normal driving, emotional distraction, cognitive distraction, and texting. Features were extracted from the eye movement data, encompassing fixation count and duration. After processing this data through machine learning models, an impressive accuracy rate of over 90% for identifying texting while driving. These findings are promising and hint at the potential for a realtime system that can detect and warn drivers when they are engaged in texting.

Keywords
Texting distraction, Driving performance, Eye tracking, Machine learning, Statistical analysis

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