Abstract-Traditional correlational analyses of human skills and highway accidents have not been ver) productive. Some of the likely reasons for this are discussed. An alternative approach to accident-cautation research is outlined in which the importance of a skill with good face validity to driving is assessed in terms of its sensitivity to frequently occurring transient human states such as fatigue, stress and alcohol intoxication. It is almost an axiom in highway safety research that traffic accidents are caused primarily by human errors, as opposed to vehicular malfunctions or adverse environmental conditions. Indeed, a recent in-depth accident investigation [Treat et al., 19771 cited human factors as definite or probable causes in 93 % of the 420 accidents under investigation. Thus, it is not surprising that a substantial amount of research has been devoted to the investigation of the correlations between accidents and performance of a variety of skills, in efforts to identify skills critical to driving. However, results of such studies (e.g. the visual-skills study of Henderson and Burg [I9741 and a variety of studies reviewed by Goldstein [I%411 generally yield disappointingly low correlations. Such findings indicate that the performance of each of a variety of skills can account for only a fraction of the variance exhibited in the accident record.

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Mental fatigue from continuous mental arithmetic is associated with an across-subjects effect of increased frontal theta and parietal alpha rhythms. A statistical classifier can use this effect to model EEG-fatigue relationships. Subjects viewed 4-digit problems on a computer, solved the problems, and pressed keys to respond. They performed until either they felt exhausted or 3 hrs had elapsed. Pre- and post-task mood scales confirmed that fatigue increased and energy decreased. We examined accuracy, response times (RT), amplitudes of ERP components N1, P2, and P300, and power of frontal theta and parietal alpha EEG rhythms over time. Mean RT rose from 6.7 s to 7.9 s but accuracy did not change significantly. The effects of time on ERP component amplitudes or latencies were inconclusive. Mean frontal EEG theta and alpha power rose by 29 % and 44%, respectively. We also used EEG frequency spectra to model the effects of time using a kernel partial least squares (KPLS) classifier, classifying EEG segments as being from the first or last 15 minutes. Accuracies ranged from 91 % to 100 % correct.

Based on Coroners ' verdicts, fatigue of car or truck drivers was a contributing factor in 9.1 % of fatal accidents involving trucks. Based on the presence of factors such as extended driving hours, falling asleep at the wheel, comments about tiredness, driving right of centre and night-time driving, the authors estimated fatigue contributed to 19.9 % of the accidents. There were approximately equal numbers of fatigued car drivers and truck drivers. An analysis of casualty and fatal truck accidents by time of day (adjusted for exposure) showed that accident risks were highest during the night on all five Victorian highways studied. Driver fatigue is one of the possible factors underlying this pattern of elevated risk. The report section described in-vehicle fatigue counter-measures. The distinction between fatigue monitors and alerting devices was made and it was recommended that eye closure and head nodding monitors and an alerting device be tested in the next stage of this project. Key Words: Fatigue (human), accident rate, fatality,