Journal article
Predicting Reduced Driver Alertness on Monotonous Highways
IEEE Pervasive Computing, Vol.14(2), pp.78-85
2015
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
Details
- Title
- Predicting Reduced Driver Alertness on Monotonous Highways
- Authors
- Gregoire S. Larue (Author) - Queensland University of TechnologyAndry Rakotonirainy (Author) - Queensland University of TechnologyAnthony N. Pettitt (Author) - Queensland University of Technology
- Publication details
- IEEE Pervasive Computing, Vol.14(2), pp.78-85
- Publisher
- Institute of Electrical and Electronics Engineers
- Date published
- 2015
- DOI
- 10.1109/MPRV.2015.38
- ISSN
- 1558-2590; 1536-1268
- Copyright note
- Copyright (c) 2015. The author's accepted version is reproduced here in accordance with the publisher's copyright policy. The definitive version is available at: http://dx.doi.org/10.1109/MPRV.2015.38
- Organisation Unit
- Road Safety Research Collaboration; School of Law and Society
- Language
- English
- Record Identifier
- 99648949702621
- Output Type
- Journal article
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