Hazard anticipation of young novice drivers - SWOV

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Keating (2007) mentioned the maturational gap caused by the difference in functional development of the socio-emotional circuits of the brain and the control circuits of the brain that is assumed by Casey et al. (2008), as a possible cause of the high crash rate of young drivers. Although recent studies applying fMRI have resulted in intriguing new insights in why adolescents tend to take more risk than adults, Paus (2009) noted that these results have to be interpreted with caution. There is a confounding interaction between age and performance. When studying agerelated changes in brain activity during the performance of a certain task, how do we know that the brain activity is different because of the performance or that the performance is different because of the brain activity? 2.3.2. Gender Male drivers aged 18-24 are more often involved in severe crashes per distance driven than female drivers in that age group (see Figure 1.1). Based on data from the Netherlands, Figure 2.3 shows how many times more young male drivers and young female drivers are involved in fatal crashes per distance driven than drivers aged 30-59 of both sexes over the period 1987 to 2008. 14 12 Male drivers (18-24) Relative Risk Ratio 10 8 6 4 2 0 1987 1988 Female drivers (18-24) 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Years 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Figure 2.3. Annual relative fatal crash risk ratio from 1987 to 2008 of male drivers and female drivers aged 18-24 with the fatal crash rate of middle-aged drivers of both sexes (aged 30-59) as reference. Source: Ministry of Infrastructure and Environment / Statistics Netherlands. 31
In 1987 the fatal crash rate (involvement in crashes that result in at least one fatality per billion driver kilometres) of young female drivers (aged 18-24) was twice the fatal crash rate of middle-aged drivers of both sexes (aged 30- 59). In this year, this relative risk ratio was 4.4 for young male drivers (aged 18-24). In 2008, the relative risk ratio of young female car drivers was 2.5 and 13.3 for young male drivers. In all years, the risk ratio of young female drivers was lower than the risk ratio of young male drivers and remained more or less constant over the years (slightly more than 2 times the crash rate of middle-aged driver). In contrast, the risk ratio of young male drivers has increased over the years. It seems as if generic road safety improvements over time (safer cars, safer roads) have had less impact on young male drivers than on young female drivers in the Netherlands. Drivers can have a crash in which no other road users are involved (e.g. when they drive against a tree). This type of crash is called a single-vehicle crash. A driver can also collide with another car. These are the car-car crashes. And a driver can collide with other types of vehicles, including pedestrians. These are the car-other type of vehicle crashes. In a fatal singlevehicle crash, a driver can kill her or himself and/or her or his passenger(s). In a car-car crash the fatalities can be in the other car, the own car or in both. In a car-other vehicle type crash the fatalities can be in the own car, the other vehicle or in both. Fatal crashes are crashes with at least one fatality. Table 2.1 shows the degree in which young male drivers cause more physical damage to themselves and other road users than young female drivers do. Using Dutch data from 2004-2008, the fatality ratios (number of fatal crashes per distance driven) for young males drivers were divided by the fatality rates for young female drivers. This relative fatality ratio (young male driver/young female driver) is presented for single-vehicle crashes, car-car crashes and car-other type of vehicle crashes. Table 2.1 also presents the relative fatality ratios of being killed by young male drivers in car-car crashes and car-other type of vehicle crashes. 32
Page 1 and 2: Hazard anticipation of young novice
Page 3 and 4: SWOV-Dissertatiereeks, Leidschendam
Page 5 and 6: Promotores: Prof. dr. W.H. Brouwer
Page 7 and 8: development of hazard perception te
Page 10 and 11: Table of contents 1. General introd
Page 14 and 15: 1. General introduction 1.1. Hazard
Page 16 and 17: 27% of all driver fatalities were d
Page 18 and 19: eported 73 car crashes during the p
Page 20 and 21: with respondents that started their
Page 22 and 23: 2. Determinants that influence haza
Page 24 and 25: planning of a trip, choice of the m
Page 26 and 27: that encompasses the entire second
Page 28 and 29: peaks at 16.7 years of age in girls
Page 30 and 31: Figure 2.2. Model of the different
Page 34 and 35: Table 2.1. Relative fatality ratios
Page 36 and 37: When do these sex differences emerg
Page 38 and 39: Functional brain differences in boy
Page 40 and 41: oth groups had in common was their
Page 42 and 43: 3. Extroversion: Extrovert persons
Page 44 and 45: is less in these brain areas, but a
Page 46 and 47: 2.4.2. Peer group influences In ado
Page 48 and 49: deduced that car drivers younger th
Page 50 and 51: or little interference with the dri
Page 52 and 53: 2.4.4. Socioeconomic and cultural b
Page 54 and 55: Preusser, 2002). The crash rate of
Page 56 and 57: impact on road safety. Despite the
Page 58 and 59: condition for the older, more exper
Page 60 and 61: person (e.g. a passenger or a pedes
Page 62 and 63: tasks was 3.10, 95% CI [1.73, 5.47]
Page 64 and 65: traffic situation is safe enough to
Page 66 and 67: 2.6.1. Exposure In Section 1.2, the
Page 68 and 69: 58 56 Male Drivers Female Drivers S
Page 70 and 71: 100 Killed car occupants aged 18-24
Page 72: less well equipped to detect and re
Page 75 and 76: 3.2. Hazard anticipation A hazard i
Page 77 and 78: Drivers are aware of hazards when t
Page 79 and 80: 1. Possible other road users on col
Page 81 and 82: oriented and the vertical axis is t
Page 83 and 84:
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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without gaze control (the gray arro
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urban areas than on rural roads, no
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the right between the parked cars i
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that in the video clips no visible
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• Young learner drivers (age rang
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• Did you have moments that you t
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difference between overt latent haz
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4.2.3. Procedure On arrival, the ex
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ecognition task, risk assessment an
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Table 4.1. Percentage of the partic
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that cognitive aspects of hazard an
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80 70 Mean percentage mentioned cov
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Table 4.3. Percentages of latent ha
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latent hazards was higher than the
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Discussion about fixated latent haz
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mentioned latent hazards, young lea
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Gaze directions and fixation durati
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4.3.3. Relationship between the two
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selection task cannot be used to te
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Underwood, Chapman et al. (2002) an
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more difficulties to detect and rec
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5. Testing hazard anticipation, an
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• The video task and the photo ta
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animation clips were made, each las
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The photo task Three experts on the
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5.2.3. Procedure Participants were
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5.2.6. Overt and covert latent haza
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The effect size was small, η 2 P =
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action selection task of Chapter 4,
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3. Effect of interruptions The inte
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5.3.3. Procedure Participants were
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The mean click score was M = 7.7 (S
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ottom-up gaze control (the tractor
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6. Simulator-based hazard anticipat
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al., 2002; Regan, Triggs, & Godley,
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esources to vehicle control (steeri
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of the University of Massachusetts
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In contrast, Ivancic & Hesketh (200
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students, whereas participants that
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for the training were based on thos
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6.2.3. Training intervention SimRAP
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A screen capture of the centre scre
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Table 6.2. (continued) critical sit
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at least sixteen of the nineteen si
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latent hazards. This is not very su
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For the control group the opposite
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The combined three near transfer si
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training program was mostly on over
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7. Discussion and conclusions 7.1.
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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
Page 246 and 247:
underlying reaction time to visual
Page 248 and 249:
Darby, P., Murray, W. & Raeside, R.
Page 250 and 251:
Fuller, R. (2007b). Motivational de
Page 252 and 253:
Irwin, D.E. (2004). Fixation locati
Page 254 and 255:
Louwerens, J.W., Gloerich, A.B.M.,
Page 256 and 257:
Murray, Å. (1998). The home and sc
Page 258 and 259:
Roth, M. (2009). Social support as
Page 260 and 261:
Ulleberg, P. (2001). Personality su
Page 262 and 263:
Summary Driving most of the times,
Page 264 and 265:
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.
Page 272:
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
Page 288:
About the author Willem Vlakveld wa
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4. On a rural road when driving str
Page 293 and 294:
they keep on walking in the same di
Page 295 and 296:
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
Page 308 and 309:
MEG Motor cortex MRI Myelination Ne
Page 310 and 311:
PFC Serotonin Striatum Synaptic pru
Page 312 and 313:
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