Hazard anticipation of young novice drivers - SWOV

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5.2.3. Procedure Participants were seated approximately 60 cm from a 15'' LCD-screen (aspect ratio 4:3). The diagonal of the photographs in the photo task was 30 cm and the diagonal of the animation video clips was 26.5 cm. This provided participants with a horizontal visual field of approximately 23° for the photo task and of approximately 20° for the video task. Before each task, written information was provided on the screen about the aim of the task and the response method. Before the actual task started, participants could practice with three photographs in case of the photo task and two video clips in case of the video task. On average, instruction and familiarisation took no longer than two minutes for the photo task and four minutes for the video task. The tasks were administered counterbalanced. It took about 25 min to complete the two tasks, including instruction and familiarisation. Participants received no financial reward for participation. 5.2.4. Data analysis A between-groups design was applied. Before testing on significant differences between groups (p < .05), the assumptions for parametric testing were checked. In all cases the criterion of normal distribution were met, but in some cases the criterion with regard to homogeneity of variance was not met. In cases the equality of variances was violated, a t-test for independent samples was applied not assuming equality of variance. Univariate analyses of variance (ANOVA) or univariate analyses of covariance (ANCOVA) were applied where more than two groups were compared. When more than two groups were compared, the Bonferroni post hoc tests were applied to check for significant differences between two groups within the three groups. Pearson's correlation coefficients (denoted as r) were calculated to express the relationship between the scores on the photo task and the video task. Multivariate analyses (MANOVA) and discriminant function analysis were also applied to examine the relationship of the scores on both tasks. Besides significance of results, the effect size (Partial èta squared, η 2 P ) was considered with η 2 P = .01 as a small, η 2 P = .06 as a medium, and η 2 P = .14 as a large effect size (Cohen, 1988). Prior to the statistical analysis the internal consistency reliability (Cronbach's α) was considered with α > .65 as acceptable. 179
5.2.5. Scores on the video task In total, participants could click on forty-three areas that were rewarded with scores. The internal reliability of the responses of all the participants of the three groups was acceptable (α = .84). Figure 5.4 presents the boxplot of the percentage of the maximum scores per group of clicks that were correct. Figure 5.4. Percentages of clicks that were correct of learner drivers, novice drivers and experienced drivers. The mean percentages correct clicks were: M = 51.5 (SE = 3.0) for learner drivers, M = 54.7 (SE = 1.9) for novice drivers and M =55.4 (SE = 2.9) for experienced drivers. ANOVA revealed that there was no significant difference between the groups, F(2,137) = 0.53, p = .59. In all three groups, male drivers did not differ significantly from female drivers. In the group of novice drivers the mean percentage correct clicks of the crash free drivers was M = 57.3 (SE = 1.8). For the drivers with at least one self-reported crash this mean percentage was M = 42.7 (SE = 7.9). A t-test for independent samples where equal variances is not assumed, showed that this difference was marginally significant, t (13.2) = 1.78, p = .098. 180
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Hazard anticipation of young novice
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SWOV-Dissertatiereeks, Leidschendam
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Promotores: Prof. dr. W.H. Brouwer
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development of hazard perception te
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Table of contents 1. General introd
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1. General introduction 1.1. Hazard
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27% of all driver fatalities were d
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eported 73 car crashes during the p
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with respondents that started their
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2. Determinants that influence haza
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planning of a trip, choice of the m
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that encompasses the entire second
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peaks at 16.7 years of age in girls
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Figure 2.2. Model of the different
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Keating (2007) mentioned the matura
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Table 2.1. Relative fatality ratios
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When do these sex differences emerg
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Functional brain differences in boy
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oth groups had in common was their
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3. Extroversion: Extrovert persons
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is less in these brain areas, but a
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2.4.2. Peer group influences In ado
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deduced that car drivers younger th
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or little interference with the dri
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2.4.4. Socioeconomic and cultural b
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Preusser, 2002). The crash rate of
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impact on road safety. Despite the
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condition for the older, more exper
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person (e.g. a passenger or a pedes
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tasks was 3.10, 95% CI [1.73, 5.47]
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traffic situation is safe enough to
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2.6.1. Exposure In Section 1.2, the
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58 56 Male Drivers Female Drivers S
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100 Killed car occupants aged 18-24
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less well equipped to detect and re
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3.2. Hazard anticipation A hazard i
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Drivers are aware of hazards when t
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1. Possible other road users on col
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oriented and the vertical axis is t
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away from me, given the particular
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the task demands. The range of acce
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Figure 3.4. Zero-risk model (Näät
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with FTD, poor monitoring of the ri
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elatively high speed in the same di
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only associated with top-down activ
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information transformation processe
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(consciously) controlled action reg
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on the left lane of a motorway (in
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3.7.1, 3.7.2 and 3.7.3. This is don
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driver does not think that she or h
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for experienced drivers even in cir
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sound was heard) and some participa
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stage. In this stage, learners comm
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A theory that explains how we may l
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were about to turn left while their
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4. Hazard anticipation, age and exp
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hazards, drivers have to imagine a
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overview of their studies: McKenna
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with regard to the risk felt in the
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Participants were requested to pres
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of the visual acuity in the fovea.
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working memory. Both bottom-up proc
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Page 132 and 133: urban areas than on rural roads, no
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Page 138 and 139: • Young learner drivers (age rang
Page 140 and 141: • Did you have moments that you t
Page 142 and 143: difference between overt latent haz
Page 144 and 145: 4.2.3. Procedure On arrival, the ex
Page 146 and 147: ecognition task, risk assessment an
Page 148 and 149: Table 4.1. Percentage of the partic
Page 150 and 151: that cognitive aspects of hazard an
Page 152 and 153: 80 70 Mean percentage mentioned cov
Page 154 and 155: Table 4.3. Percentages of latent ha
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Page 158 and 159: Discussion about fixated latent haz
Page 160 and 161: mentioned latent hazards, young lea
Page 162 and 163: Gaze directions and fixation durati
Page 164 and 165: 4.3.3. Relationship between the two
Page 166 and 167: selection task cannot be used to te
Page 168 and 169: Underwood, Chapman et al. (2002) an
Page 170 and 171: more difficulties to detect and rec
Page 172 and 173: 5. Testing hazard anticipation, an
Page 174 and 175: • The video task and the photo ta
Page 176 and 177: animation clips were made, each las
Page 178 and 179: The photo task Three experts on the
Page 182 and 183: 5.2.6. Overt and covert latent haza
Page 184 and 185: The effect size was small, η 2 P =
Page 186 and 187: action selection task of Chapter 4,
Page 188 and 189: 3. Effect of interruptions The inte
Page 190 and 191: 5.3.3. Procedure Participants were
Page 192 and 193: The mean click score was M = 7.7 (S
Page 194 and 195: ottom-up gaze control (the tractor
Page 196 and 197: 6. Simulator-based hazard anticipat
Page 198 and 199: al., 2002; Regan, Triggs, & Godley,
Page 200 and 201: esources to vehicle control (steeri
Page 202 and 203: of the University of Massachusetts
Page 204 and 205: In contrast, Ivancic & Hesketh (200
Page 206 and 207: students, whereas participants that
Page 208 and 209: for the training were based on thos
Page 210 and 211: 6.2.3. Training intervention SimRAP
Page 212 and 213: A screen capture of the centre scre
Page 214 and 215: Table 6.2. (continued) critical sit
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Page 220 and 221: For the control group the opposite
Page 222 and 223: The combined three near transfer si
Page 224 and 225: training program was mostly on over
Page 226 and 227: 7. Discussion and conclusions 7.1.
Page 228 and 229: ehaviour could be the cause of unsa
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transport. It can also be status, a
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of this is an intervention in the a
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performance in hazard detection and
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than both other groups. It is likel
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etter scanning for latent hazards i
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hazards are a good indicator of som
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order to avoid attribution of an ex
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References Abelson, R.P. (1981). Ps
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underlying reaction time to visual
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Darby, P., Murray, W. & Raeside, R.
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Fuller, R. (2007b). Motivational de
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Irwin, D.E. (2004). Fixation locati
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Louwerens, J.W., Gloerich, A.B.M.,
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Murray, Å. (1998). The home and sc
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Roth, M. (2009). Social support as
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Ulleberg, P. (2001). Personality su
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Summary Driving most of the times,
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Deficit Hyperactivity Disorder (ADH
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framework of Brouwer & Schmidt (200
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and older learner drivers. From the
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prepare participants for the tasks.
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group than before the training and
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verkeer. Het probleem van de jonge
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status en een auto betekent ook vri
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Belangrijk is dat zowel bij zichtba
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en ervaren bestuurders (Huestegge e
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afgerond, hadden ook een significan
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om hun veiligheidsmarge te vergrote
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About the author Willem Vlakveld wa
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4. On a rural road when driving str
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they keep on walking in the same di
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5. The driver drivers straight on a
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Appendix 3 Additional latent hazard
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Appendix 4 Glossary of abbreviation
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economic progress and world trade.
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Appendix 5 Glossary on brain issues
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Gonadotropin releasing hormone Gray
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MEG Motor cortex MRI Myelination Ne
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PFC Serotonin Striatum Synaptic pru
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SWOV-Dissertatiereeks In deze reeks
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