01046302

Component

https://doi.org/10.3141/2018-01

http://scholar.google.com...eyes&publication_year=2007

Driver distraction has become an important and growing safety concern as the use of in-vehicle information systems (IVISs), such as cell phones and navigation systems, continues to increase. One approach to allowing people to benefit from IVISs without compromising safety is to create adaptive IVISs that adjust their functions according to driver and roadway state. A critical element of adaptive IVISs involves monitoring driver distraction in real time; with such a monitoring function it is possible to mitigate that distraction. This study applied Bayesian networks (BNs), a data mining method, to develop a real-time approach to detecting cognitive distraction using drivers’ eye movements and driving performance. Data were collected in a simulator experiment involving 10 participants who interacted with an IVIS system while driving. BN models were trained and tested to investigate the influence of three model characteristics on distraction detection: time history of driver behavior, inclusion of hidden nodes in the model structure, and how data are summarized and the length of training sequences. Results showed that BNs could identify driver distraction reliably with an average accuracy of 80.1%. Dynamic BNs (DBNs) that consider time dependencies of driver behavior produced more sensitive models than static BNs (SBNs). Longer training sequences improved DBN model performance. Blink frequency and fixation measures were particularly indicative of distraction. These results demonstrate that BNs, especially DBNs, can detect driver cognitive distraction by extracting information from complex behavioral data. Potential applications include the design of adaptive in-vehicle systems and the evaluation of driver distraction.

01088239

English

pp 1-8

2007

Transportation Research Record: Journal of the Transportation Research Board

9780309104456

Print

Figures (4) ; References (29) ; Tables (2)

Advanced driver information systems; Behavior; Cognitive impairment; Distraction; Eye movements; Highway safety; Real time information

Bayesian networks; Cognitive factors

Highways; Safety and Human Factors; I83: Accidents and the Human Factor

TRIS, TRB, ATRI

Feb 8 2007 7:48PM

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