Article

Fatigue, sleepiness, and performance in simulated versus real driving conditions.

Clinique du Sommeil, CHU Pellegrin, Bordeaux, France.
Sleep (Impact Factor: 5.06). 12/2005; 28(12):1511-6.
Source: PubMed

ABSTRACT To determine whether real-life driving would produce different effects from those obtained in a driving simulator on fatigue, performances and sleepiness.
Cross-over study involving real driving (1200 km) or simulated driving after controlled habitual sleep (8 hours) or restricted sleep (2 hours).
Sleep laboratory and open French Highway.
Twelve healthy men (mean age +/- SD = 21.1 +/- 1.6 years, range 19-24 years, mean yearly driving distance +/- SD = 6563 +/- 1950 miles) free of sleep disorders.
Self-rated fatigue and sleepiness, simple reaction time before and after each session, number of inappropriate line crossings from the driving simulator and from video-recordings of real driving.
Line crossings were more frequent in the driving simulator than in real driving (P < .001) and were increased by sleep deprivation in both conditions. Reaction times (10% slowest) were slower during simulated driving (P = .004) and sleep deprivation (P = .004). Subjects had higher sleepiness scores in the driving simulator (P = .016) and in the sleep restricted condition (P = .001). Fatigue increased over time (P = .011) and with sleep deprivation (P = .000) but was similar in both driving conditions.
Fatigue can be equally studied in real and simulated environments but reaction time and self-evaluation of sleepiness are more affected in a simulated environment. Real driving and driving simulators are comparable for measuring line crossings but the effects are of higher amplitude in the simulated condition. Driving simulator may need to be calibrated against real driving in various condition.

Download full-text

Full-text

Available from: Nicholas D Moore, Jul 06, 2015
1 Follower
 · 
179 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper provides a critical review of laboratory-based studies of spatial attention. We highlight a number of ways in which such studies fail to capture the key factors/constraints that have been shown to give rise to an increased risk of vehicular accident in real-world situations. In particular, limitations that are related to the design of the attentional capture task itself and limitations that are concern the demographic and current state of the participants tested in these laboratory studies are discussed. A list of recommendations are made concerning those areas in which laboratory-based spatial attention research could focus on in the future in order to make sure that their results are more relevant to those working in an applied setting, and thus, enhance translational research.
    08/2014; 44(4):524-530. DOI:10.1109/THMS.2014.2316502
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Currently, the legal, technical and psychological regulatory framework of automated driving is being discussed by car manufacturers and researchers to guarantee its safe and smooth introduction into the traffic system. This discussion is accompanied by plenty of studies that seek to study the human side of the interaction with automation and to expose potential problems and hazards. Past research from other domains has shown that the studies' subjects differ considerably, for example in their abilities (e.g. ability to monitor) or in their attitudes (e.g. trust in automation). In this work we discuss potential individual differences – classified into dispositions, stable traits, operator state, attitudes and demographics – that could influence the human performance in interactions with automation. Where they exist, valid methods of measurement are referenced. The review closes with a deduction of potential risk groups that were inferred based on the reviewed literature.
    Interacción '14: Proceedings of the XV International Conference on Human Computer Interaction; 01/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Despite growing interest over the decades, the ques- tion of estimating cognitive workload of operators involved in complex multitask operations, such as helicopter pilots, remains a key issue. One of the main difficulties facing workload inference models is that no single specific indicator of workload exists, so that multiple sources of information have to be inputted to the model. The question then arises as to the nature and the quantity of features to be used for increasing model performance. In this research, done in cooperation with Eurocopter, the effectiveness of physiological, psychological, and cognitive features for estimating helicopter pilots’ workload was systematically investigated, using Bayesian networks (BNs). The study took place in two different contexts: a constrained laboratory situation with low ecological validity and a more realistic and challenging situation relying on virtual reality. The constrained conditions of the laboratory study allowed us for testing various combinations of entropy-based physiological, cognitive, and affect features as inputs of BN mod- els. These three different kinds of features are shown to carry complementary information that can be used with advantage by the model. The results also suggest that increasing the number of physiological inputs improves the model performance. The second study aimed at challenging some of these conclusions in a more ecological context, by using the NH90 full-flight simulator of the Helisim company. The results emphasize the problem of accessing the ground truth, as well as the need for an efficient feature selection or extraction step prior to the classification step.
    IEEE Transactions on Intelligent Transportation Systems 12/2013; 14(4):1872-1881. DOI:10.1109/TITS.2013.2269679 · 2.47 Impact Factor