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Dokument 1.pdf - ELBA: Das elektronische BASt-Archiv - hbz

Dokument 1.pdf - ELBA: Das elektronische BASt-Archiv - hbz

Dokument 1.pdf - ELBA: Das elektronische BASt-Archiv - hbz

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4<br />

Assessment of driver fatigue<br />

Driver fatigue is a contributing factor in an<br />

estimated 10-20% of road accidents. A huge<br />

number of different methods is available to assess<br />

driver fatigue. The project’s goal was to describe<br />

and compare the strengths and weaknesses of<br />

different methods to assess driver fatigue and to<br />

present an overview of the existing fatigue<br />

detection and fatigue warning systems. The<br />

evaluation of these methods and systems is based<br />

on literature analysis and interviews with experts<br />

and users. The following procedures were taken:<br />

• Literature analysis and overview of physiological<br />

and performance-based methods to assess<br />

driver fatigue.<br />

• Literature analysis of fatigue detection and<br />

warning systems, interviews with their<br />

manufacturers and overview of the identified<br />

systems.<br />

• Interviews with 20 users of currently in middleand<br />

upper-class cars implemented fatigue<br />

warning systems to assess the perceived quality<br />

of fatigue detection, driver acceptance and<br />

compliance.<br />

• Development of a list of criteria to assess and<br />

compare the quality of the most valid methods to<br />

assess driver fatigue.<br />

• Two-stage survey (Delphi-Study) with 12<br />

experts from academic and industrial research<br />

areas. It was aimed at the selection and<br />

evaluation of the most valid methods to assess<br />

driver fatigue, their subsequent re-evaluation<br />

providing explanatory statements in case of<br />

contradicting opinions, and the assessment of<br />

the methods’ suitability for different fields of<br />

application.<br />

users’ point of view. Several drivers continue to<br />

drive even despite of a system’s warning that they<br />

perceive as accurate. About half of them perceive<br />

fatigue warning systems as a gain in safety.<br />

The most valid methods to assess driver fatigue<br />

selected by the 12 experts are the assessment of<br />

driving performance (steering behaviour and lane<br />

keeping) and eyelid closure, video-based expert<br />

assessments, EEG and (quite controversial) the<br />

Pupillographic Sleepiness Test. Their differing<br />

strengths and weaknesses are extensively<br />

described in the report at hand.<br />

There is still no gold standard of fatigue<br />

assessment. Depending on the fields of application<br />

all six of the selected methods (research &<br />

development), only a few (fatigue warning systems)<br />

or none of them (traffic controls) are applicable.<br />

When choosing a method the goal and context of<br />

fatigue assessment and the strengths and<br />

weaknesses of the respective methods should be<br />

considered. The combination of at least two<br />

different methods is recommended to achieve a<br />

valid assessment of driver fatigue.<br />

Fatigue detection systems and the most promising<br />

methods to assess driver fatigue should be further<br />

optimized and accompanying studies need to be<br />

conducted. Further measures to effectively reduce<br />

the number of accidents were discussed during the<br />

workshop and presented in the report at hand.<br />

• Workshop with the experts that participated in<br />

the Delphi-Study to discuss and complement the<br />

obtained results.<br />

70 fatigue detection systems were identified. They<br />

differ in their underlying methods to assess fatigue,<br />

their availability and design of driver feedback, and<br />

in their prevalence. Convincing evidence of their<br />

validity or the percentage of false and missed<br />

alarms are rarely reported. The fatigue warning<br />

systems that are more and more implemented in<br />

middle- and upper-class cars often fail to provide a<br />

satisfying fatigue detection performance from a

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