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.
Table 1.
Definition of the studied measures and units in which they were recorded.
n corresponds to an undefined unity, m to meters, s to seconds, km to kilometers, h to hours and log to logarithm.
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.
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.
Table 2.
Statistical age groups comparisons between the three age groups.
When mean speed was correlated with the driving measure considered, an ANCOVA controlling for mean speed was used. When mean speed appeared to be uncorrelated with the driving measure considered, a parametric ANOVA was used. For non-normally distributed driving measures bootstrap-based ANOVA were used. The p-values resulting from the pairwise comparisons between Inexperienced, Experienced and Older drivers are shown on the three most right columns. For comparisons showing a significant difference or a strong tendency, an arrow indicates the direction of the difference.
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.
Table 3.
Statistical age groups comparisons between the three age groups during the Highway scenario.
When mean speed was correlated with the driving measure considered, an ANCOVA controlling for mean speed was used. When mean speed appeared to be uncorrelated with the driving measure considered, an ANOVA was used. For non-normally distributed driving measures bootstrap-based ANOVA were used. The p-values resulting from the pairwise comparisons between Inexperienced, Experienced and Older drivers are shown on the three most right columns. For comparisons showing a significant difference or a strong tendency, an arrow indicates the direction of the difference.
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.
Table 4.
Statistical comparison between the three age groups during the Rural scenario.
When mean speed was correlated with the driving measure considered, an ANCOVA controlling for mean speed was used. When mean speed appeared to be uncorrelated with the driving measure considered, an ANOVA was used. For non-normally distributed driving measures bootstrap-based ANOVA were used. The p-values resulting from the pairwise comparisons between Unexperienced, Experienced and Older drivers are shown on the three most right columns. For comparisons evidencing a significant difference, an arrow indicates the direction of the difference.
Fig 6.
Correlation between NeuroTracker speed thresholds (represented in log units) and mean speeds naturally adopted in the rural scenario.
Table 5.
The 3D-MOT as a predictor of a risky driving behavior.
Bivariate correlations between perceptual-cognitive measure and driving measures across the three scenarios.
Table 6.
The 3D-MOT as a predictor of a risky driving behavior.
Multiple linear regression analyses performed on measures recorded during the rural scenario. 3D-MOT scores, Age and Mean driving speed were entered as predictors in the model. For each driving measure, regression weights (β) and significance value (p) are shown.