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Comparative Study
. 2017 Oct 10;12(10):e0185909.
doi: 10.1371/journal.pone.0185909. eCollection 2017.

Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups

Jesse Michaels  1 , Romain Chaumillon  1 , David Nguyen-Tri  1 , Donald Watanabe  1 , Pierro Hirsch  2 , Francois Bellavance  3 , Guillaume Giraudet  1   4 , Delphine Bernardin  1   5 , Jocelyn Faubert  1
Affiliations
Comparative Study

Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups

Jesse Michaels et al. PLoS One. .

Abstract

To investigate the links between mental workload, age and risky driving, a cross-sectional study was conducted on a driving simulator using several established and some novel measures of driving ability and scenarios of varying complexity. A sample of 115 drivers was divided into three age and experience groups: young inexperienced (18-21 years old), adult experienced (25-55 years old) and older adult (70-86 years old). Participants were tested on three different scenarios varying in mental workload from low to high. Additionally, to gain a better understanding of individuals' ability to capture and integrate relevant information in a highly complex visual environment, the participants' perceptual-cognitive capacity was evaluated using 3-dimensional multiple object tracking (3D-MOT). Results indicate moderate scenario complexity as the best suited to highlight well-documented differences in driving ability between age groups and to elicit naturalistic driving behavior. Furthermore, several of the novel driving measures were shown to provide useful, non-redundant information about driving behavior, complementing more established measures. Finally, 3D-MOT was demonstrated to be an effective predictor of elevated crash risk as well as decreased naturally-adopted mean driving speed, particularly among older adults. In sum, the present experiment demonstrates that in cases of either extreme high or low task demands, drivers can become overloaded or under aroused and thus task measures may lose sensitivity. Moreover, insights from the present study should inform methodological considerations for future driving simulator research. Importantly, future research should continue to investigate the predictive utility of perceptual-cognitive tests in the domain of driving risk assessment.

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Conflict of interest statement

Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Jocelyn Faubert is director of the Visual Psychophysics and Perception Laboratory at the University of Montreal and he is the Chief Science Officer of CogniSens Athletics Inc. who produce the commercial version of the 3D-MOT program (NeuroTracker) used in this study. In this capacity, he holds shares in the company. Pierro Hirsch is a road safety researcher and driver training program developer at Virage Simulation Inc. who produces the commercial version of the driving simulator (VS500M) used in this study. Delphine Bernardin and Guillaume Giraudet are associated professors of the NSERC/Essilor Chair at the University of Montreal and are both employed by Essilor as research project managers. No author listed in the statement above contributed to data collection or analysis. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Image of the VS500M driving simulator and example of events belonging to the single visible conflict category within the Urban, Rural and Highway scenario.
The event displayed on the top right panel (i.e. Urban scenario) corresponds to a cyclist violating the red light. During the events displayed in the bottom left panel (i.e. Rural scenario) and in the bottom right panel (i.e. Highway scenario) a car gets out of the drive-way and comes into the trajectory of the participant's car. These three events differed slightly to avoid participants’ anticipation but belong to the same category of events. Each panel corresponds to a photograph of the simulator’s central screen.
Fig 2
Fig 2. Graphical representation of the correlational analysis on the aggregated dataset performed using hierarchical clustering analysis in the R statistical environment (R Development Core Team, 2008).
The size and color of each circle represents the magnitude and the direction of the correlation, respectively. Note that only the significant correlations (p< .05) appear on this graphical representation. As a striking result, the data can be clearly shown to be distributed into two clusters: one with positive correlations centered on Mean Speed and the other with negative correlations between speed measures and distance measures.
Fig 3
Fig 3. Graphical representation of the hcluster correlation analysis on the ‘Urban Scenario’ dataset controlling for mean speed.
Only the significant correlations (p< .05) appear on this graphical representation.
Fig 4
Fig 4. Graphical representation of the hcluster correlation analysis computed in R on the ‘Highway scenario’ dataset controlling for mean speed.
Only the significant correlations (p< .05) appear on this graphical representation.
Fig 5
Fig 5. Graphical representation of the hcluster correlation analysis computed in R on the ‘Rural Scenario’ dataset controlling for mean speed.
Only the significant correlations (p< .05) appear on this graphical representation.
Fig 6
Fig 6. Correlation between NeuroTracker speed thresholds (represented in log units) and mean speeds naturally adopted in the rural scenario.
See this image and copyright information in PMC

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Publication types

Grants and funding

This research was supported by the Société de l'assurance automobile Québec/Fonds de recherche Société et culture (2012-OU-146156) (https://saaq.gouv.qc.ca/en/ & http://www.frqsc.gouv.qc.ca/en/), with continued work funded by Natural Sciences and Engineering Research Council of Canada/Essilor (http://www.nserc-crsng.gc.ca/index_eng.asp & www.essilor.ca) Industrial Research Chair awarded to JF (IRCPJ305729-13). The funders provided support in the form of salaries for authors DB, GG and PH, but did not have any additional role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions’ section.

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