Predicting reduced driver alertness on monotonous highways : a driving simulator study

Larue, Gregoire S., Rakotonirainy, Andry, & Pettitt, Anthony N. (2015) Predicting reduced driver alertness on monotonous highways : a driving simulator study. IEEE Pervasive Computing, 14(2), pp. 78-85.

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Abstract

Impaired driver alertness increases the likelihood of drivers’ making mistakes and reacting too late to unexpected events while driving. This is particularly a concern on monotonous roads, where a driver’s attention can decrease rapidly. While effective countermeasures do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behavior in real-time. The aim of this study is to predict drivers’ level of alertness through surrogate measures collected from in-vehicle sensors. Electroencephalographic activity is used as a reference to evaluate alertness. Based on a sample of 25 drivers, data was collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device. Various classification models were tested from linear regressions to Bayesians and data mining techniques. Results indicated that Neural Networks were the most efficient model in detecting lapses in alertness. Findings also show that reduced alertness can be predicted up to 5 minutes in advance with 90% accuracy, using surrogate measures such as time to line crossing, blink frequency and skin conductance level. Such a method could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring, in real-time, drivers' behavior on highways.

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ID Code: 78718
Item Type: Journal Article
Refereed: Yes
Keywords: safety, time predictions, driver alertness modeling, machine learning techniques
DOI: 10.1109/MPRV.2015.38
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AUTOMOTIVE ENGINEERING (090200)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Australian and New Zealand Standard Research Classification > TECHNOLOGY (100000) > OTHER TECHNOLOGY (109900)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > PSYCHOLOGY (170100) > Sensory Processes Perception and Performance (170112)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Psychology & Counselling
Copyright Owner: Copyright 2014 IEEE
Deposited On: 17 Nov 2014 23:25
Last Modified: 20 Nov 2015 03:12

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