Real-time evaluation of driver’s alertness on highways

Larue, Gregoire S., Rakotonirainy, Andry, & Pettitt, Anthony N. (2011) Real-time evaluation of driver’s alertness on highways. In Pratelli, A & Brebbia, C A (Eds.) Urban Transport XVII, Wessex Institute of Technology Press, Pisa.

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Suburbanisation has been internationally a major phenomenon in the last decades. Suburb-to-suburb routes are nowadays the most widespread road journeys; and this resulted in an increment of distances travelled, particularly on faster suburban highways. The design of highways tends to over-simplify the driving task and this can result in decreased alertness. Driving behaviour is consequently impaired and drivers are then more likely to be involved in road crashes. This is particularly dangerous on highways where the speed limit is high. While effective countermeasures to this decrement in alertness do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behaviour in real-time. The aim of this study is to evaluate in real-time the level of alertness of the driver through surrogate measures that can be collected from in-vehicle sensors. Slow EEG activity is used as a reference to evaluate driver's alertness. Data are collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device (N=25 participants). Four different types of highways (driving scenario of 40 minutes each) are implemented through the variation of the road design (amount of curves and hills) and the roadside environment (amount of buildings and traffic). We show with Neural Networks that reduced alertness can be detected in real-time with an accuracy of 92% using lane positioning, steering wheel movement, head rotation, blink frequency, heart rate variability and skin conductance level. Such results show that it is possible to assess driver's alertness with surrogate measures. Such methodology could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring in real-time drivers' behaviour on highways, and therefore it could result in improved road safety.

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ID Code: 41996
Item Type: Conference Paper
Refereed: Yes
Additional Information: Organiser: University of Pisa, Italy & Wessex Institute of Technology, UK
Additional URLs:
Keywords: Driving impairment, Alertness, Neural Networks, Simulated driving, Real-time assessment
DOI: 10.2495/UT110471
ISBN: 978-1-84564-520-5
ISSN: 1743-3509
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) > Applied Mathematics not elsewhere classified (010299)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AUTOMOTIVE ENGINEERING (090200) > Automotive Safety Engineering (090204)
Australian and New Zealand Standard Research Classification > TECHNOLOGY (100000) > OTHER TECHNOLOGY (109900) > Technology not elsewhere classified (109999)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Public Health and Health Services not elsewhere classified (111799)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Computer Perception Memory and Attention (170201)
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute of Health and Biomedical Innovation
Past > Schools > Mathematical Sciences
Current > Schools > School of Psychology & Counselling
Copyright Owner: Copyright 2011 [please consult the authors]
Deposited On: 15 Jun 2011 23:29
Last Modified: 21 Jun 2011 19:44

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