440 research outputs found
Commentaries and Responses to "The Driver Behaviour Questionnaire as a predictor of accidents: A meta-analysis" [Commentaries lead by Anders af Wahlberg; Responses lead by J.C.F. de Winter]
No full textThe following discussion is in response to a 2010 article published in the Journal of Safety Research by J.C.F. de Winter and D. Dodou entitled “The Driver Behaviour Questionnaire as a predictor of accidents: A meta-analysis” (Volume 41, Number 6, pp. 463-470, available on sciencedirect.com). The editors are pleased to provide a forum for this exchange and welcome further comments
A quarter of a century of the DBQ: some supplementary notes on its validity with regard to accidents
No full textThis article synthesises the latest information on the relationship between the Driver Behaviour Questionnaire (DBQ) and accidents. We show by means of computer simulation that correlations with accidents are necessarily small because accidents are rare events. An updated meta-analysis on the zero-order correlations between the DBQ and self-reported accidents yielded an overall r of .13 (fixed-effect and random-effects models) for violations (57,480 participants; 67 samples) and .09 (fixed-effect and random-effects models) for errors (66,028 participants; 56 samples). An analysis of a previously published DBQ dataset (975 participants) showed that by aggregating across four measurement occasions, the correlation coefficient with self-reported accidents increased from .14 to .24 for violations and from .11 to .19 for errors. Our meta-analysis also showed that DBQ violations (r = .24; 6353 participants; 20 samples) but not DBQ errors (r = - .08; 1086 participants; 16 samples) correlated with recorded vehicle spee
Effects of adaptive cruise control and highly automated driving on workload and situation awareness: a review of the empirical evidence
No full textAdaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway
Supplementary materials for the article: Five-Point Likert Items: t test versus Mann-Whitney-Wilcoxon
No full textSupplementary materials for the article: De Winter, J. C.F., & Dodou, D. (2010). Five-point Likert items: t test versus Mann-Whitney-Wilcoxon. Practical Assessment, Research & Evaluation, 15, 1-12
Supplementary data for the following paper "Effects of mental demands on situation awareness during platooning: A driving simulator study"
No full textSupplementary data for the following paper: Heikoop, D., De Winter, J. C. F., Van Arem, B., & Stanton, N. A. (2018). Effects of mental demands on situation awareness during platooning: A driving simulator study. Transportation Research Part F.</span
Supplementary materials for the article: Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size.
No full textSupplementary materials for the article: De Winter, J. C. F., & Dodou, D. (2012). Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics, 39, 695-710. https://doi.org/10.1080/02664763.2011.61044
The effects of driving with different levels of unreliable automation on self-reported workload and secondary task performance
No full textUntil automated cars function perfectly, drivers will have to take over control when automation fails or reaches its functional limits. Two simulator experiments (N = 24 and 27) were conducted, each testing four automation levels ranging from manual control (MC) to highly automated driving. In both experiments, participants about once every 3 min experienced an event that required intervention. Participants performed a secondary divided attention task while driving. Automation generally resulted in improved secondary task performance and reduced self-reported physical demand and effort as compared to MC. However, automated speed control was experienced as more frustrating than MC. Participants responded quickly to the events when the stimulus was salient (i.e., stop sign, crossing pedestrian, and braking lead car), but often failed to react to an automation failure when their vehicle was driving slowly. In conclusion, driving with imperfect automation can be frustrating, even though mental and physical demands are reduced
Supplementary data for the paper: Situation awareness based on eye movements in relation to the task environment.
No full textSupplementary data for the paper: de Winter, J.C.F., Eisma, Y.B., Cabrall, C.D.D., Hancock, P.A., and Stanton, N.A.; Situation awareness based on eye movements in relation to the task environment; Cognition, Technology and Work
Book review of: Modeling Human–System Interaction: Philosophical and Methodological Considerations, With Examples By Thomas B. Sheridan
No full textModeling Human–System Interaction: Philosophical and Methodological Considerations, With Examples By Thomas B. Sheridan 2017, 192 pages, $110.00 Hoboken, NJ: John Wiley & Sons, Inc. ISBN 978-1-119-275268-2Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Human-Robot InteractionBiomechatronics & Human-Machine Contro
Predicting self-reported violations among novice license drivers using pre-license simulator measures
No full textNovice drivers are overrepresented in crash statistics and there is a clear need for remedial measures. Driving simulators allow for controlled and objective measurement of behavior and might therefore be a useful tool for predicting whether someone will commit deviant driving behaviors on the roads. However, little is currently known about the relationship between driving-simulator behavior and on-road driving behavior in novice drivers. In this study, 321 drivers, who on average 3.4 years earlier had completed a pre-license driver-training program in a medium-fidelity simulator, responded to a questionnaire about their on-road driving. Zero-order correlations showed that violations and speed in the simulator were predictive of self-reported on-road violations. This relationship persisted after controlling for age, gender, mileage, and education level. Respondents with a higher number of violations, faster speed, and lower number of errors in the simulator reported completing fewer hours of on-road lessons before their first on-road driving test. The results add to the literature on the predictive validity of driving simulators, and can be used to identify at-risk drivers early in a driver-training program.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Medical Instruments & Bio-Inspired Technolog
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