Why does a driver need to process visual information quickly.

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Whether in a driver education class or an empty parking lot, putting in the hours of trial and error necessary to earn a driver’s license is, for many, a right of passage that marks a step from adolescence into adulthood. But while novice drivers of any age may be itching to get out on the open road, they’re also among the most likely to be involved in a crash, with traffic fatalities constituting the leading cause of death for those between the ages of 15 and 29.

“It is widely agreed that it takes time and experience to become a fully safe and competent driver,” writes Chole Robbins and Peter Chapman of the University of Nottingham in Accident Analysis and Prevention. While new drivers must demonstrate they possess the motor skills necessary to control a vehicle during the licensing process, the researchers explain, it can take years for motorists to fully develop the higher-order cognitive processes required to navigate safely — a performance boost that seems to arise in part from learning how to look at the road.

Through a meta-analysis of 18 studies including 318 novice drivers and 320 experienced drivers, Robbins and Chapman found that motorists who had operated a vehicle for 3 or more years scanned a wider swath of the road ahead, allowing them to detect potential hazards in their peripheral vision more effectively.

“Novice drivers [display] a narrower spread of horizontal search compared to more experienced drivers, suggesting that novice drivers do not anticipate and scan for potential hazards to either side of them,” the researchers explain.

The studies employed a variety of methodologies ranging from simple driving simulations where participants passively observed a scene, often with limited peripheral vision, to more immersive simulations and on-road studies that required individuals to actively control a vehicle while accounting for their surroundings. In each case, participants’ eye movements were tracked to measure fixation duration, horizontal spread of search, vertical spread of search, and/or number of fixations throughout the task.

Overall, the researchers found that while “failure to look properly” is one of the most frequent causes of driver error reported by police, the only difference between experienced and inexperienced drivers eye movements that stood up to meta-analysis was drivers’ horizontal spread of search. This suggests that visual training interventions for drivers, which often focus on teaching drivers to look further down the road, might benefit more from increasing the breadth of drivers’ vision, Robbins and Chapman conclude.

Changes in drivers’ eye movements have also been found to contribute to perceptual impairments while driving under the influence of alcohol.

In a study of 15 participants in Psychological Science, Mark Nawrot (North Dakota State University) and colleagues tracked individuals’ eye movements while they completed a series of motion perception tasks both sober and intoxicated. These tasks included matching the depth of visual stimuli, following a dot visually as it moved on screen, and remaining fixated on a target in light and dark conditions. When participants had a blood alcohol concentration level of at least 0.08%, at which point a person is legally considered intoxicated, they also had more difficulty visually pursuing moving objects, which in turn contributed to reduced depth perception.

Eye movements allow drivers to both find and fixate on visual information in the environment, helping them to avoid potential hazards by providing vital information about their direction of travel, Nawrot and colleagues explain.

“Intoxicated drivers may have difficulty determining the relative position of obstacles,” Nawrot and colleagues conclude.

References

Nawrot, M., Nordenstrom, B., & Olson, A. (2004). Disruption of eye movements by ethanol intoxication affects perception of depth from motion parallax. Psychological Science, 15(12), 858–865. doi: 10.1111/j.0956-7976.2004.00767.x

Robbins, C., & Chapman, P. (2019). How does drivers’ visual search change as a function of experience? A systematic review and meta-analysis. Accident Analysis & Prevention, 132, 105266. doi: 10.1016/j.aap.2019.105266

1. Almahasneh, H., Chooi, W. T., Kamel, N., & Malik, A. S. (2014). Deep in thought while driving: An EEG study on drivers’ cognitive distraction. Transport Res F: Traffic Psychol Behav, 26, 218–226. https://doi.org/10.1016/j.trf.2014.08.001.

   Article  Google Scholar 

2. Antonson, H., Ahlström, C., Mårdh, S., Blomqvist, G., & Wiklund, M. (2014). Landscape heritage objects’ effect on driving: A combined driving simulator and questionnaire study. Accid Anal Prev, 62, 168–177. https://doi.org/10.1016/j.aap.2013.09.021.

   Article  Google Scholar 

3. Burns, P. C., Parkes, A., Burton, S., Smith, R. K., & Burch, D. (2002). How dangerous is driving with a mobile phone? Benchmarking the impairment to alcohol (no. TRL547). Berks: Transport Research Laboratory https://trl.co.uk/sites/default/files/TRL547.pdf.

   Google Scholar 

4. Castro, C. (2008). Visual demands and driving. In C. Castro (Ed.), Human factors of visual and cognitive performance in driving (pp. 2–26). Boca Raton: CRC Press.

   Chapter  Google Scholar 

5. Charlton, S. G., & Starkey, N. J. (2013). Driving on familiar roads: Automaticity and inattention blindness. Transport Res F: Traffic Psychol Behav, 19, 121–133. https://doi.org/10.1016/j.trf.2013.03.008.

   Article  Google Scholar 

6. Charlton, S. G., & Starkey, N. J. (2017). Drivers’ mental representations of familiar rural roads. J Environ Psychol, 50, 1–8. https://doi.org/10.1016/j.jenvp.2017.01.003.

   Article  Google Scholar 

7. Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol Methods, 1, 16–29. https://doi.org/10.1037/1082-989X.1.1.16.

   Article  Google Scholar 

8. Dingus, T. A., Guo, F., Lee, S., Antin, J. F., Perez, M., Buchanan-King, M., & Hankey, J. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc Natl Acad Sci, 113(10), 2636–2641. https://doi.org/10.1073/pnas.1513271113.

   Article  Google Scholar 

9. Dukic, T., Ahlstrom, C., Patten, C., Kettwich, C., & Kircher, K. (2013). Effects of Electronic Billboards on Driver Distraction. Traffic Injury Prevention, 14(5), 469–76. https://doi.org/10.1080/15389588.2012.731546.

   Article  Google Scholar 

10. Galéra, C., Orriols, L., M’Bailara, K., Laborey, M., Contrand, B., Ribéreau-Gayon, R., Masson, F., Bakiri, S., Gabaude, C., Fort, A., Maury, B., Lemercier, C., Cours, M., Bouvard, M. P., & Lagarde, E. (2012). Mind wandering and driving: Responsibility case-control study. BMJ, 345. https://doi.org/10.1136/bmj.e8105.

    Article  Google Scholar 

11. Garrison, T. M., & Williams, C. C. (2013). Impact of relevance and distraction on driving performance and visual attention in a simulated driving environment. Appl Cogn Psychol, 27(3), 396–405. https://doi.org/10.1002/acp.2917.

    Article  Google Scholar 

12. Haigney, D. E., Taylor, R. G., & Westerman, S. J. (2000). Concurrent mobile (cellular) phone use and driving performance: Task demand characteristics and compensatory processes. Transport Res F: Traffic Psychol Behav, 3(3), 113–121. https://doi.org/10.1016/S1369-8478(00)00020-6.

    Article  Google Scholar 

13. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. New Jersey: Prentice Hall.

    Google Scholar 

14. Harbluk, J. L., Noy, Y. I., Trbovich, P. L., & Eizenman, M. (2007). An on-road assessment of cognitive distraction: Impacts on drivers’ visual behavior and braking performance. Accid Anal Prev, 39(2), 372–379. https://doi.org/10.1016/j.aap.2006.08.013.

    Article  Google Scholar 

15. Hedlund, J., Simpson, H., & Mayhew, D. (2006). Summary of Proceedings and Recommendations. Presented at the international conference on distracted driving. Ottawa: The Traffic Injury Research Foundation, The Canadian Automobile Association.

16. Horberry, T., Anderson, J., Regan, M. A., Triggs, T. J., & Brown, J. (2006). Driver distraction: The effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. Accid Anal Prev, 38(1), 185–191. https://doi.org/10.1016/j.aap.2005.09.007.

    Article  Google Scholar 

17. Hoyle, R. H. (2012). Handbook of structural equation modeling. New York: The Guilford Press.

    Google Scholar 

18. Kaber, D. B., Liang, Y., Zhang, Y., Rogers, M. L., & Gangakhedkar, S. (2012). Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior. Transportation research part F: traffic psychology and behavior, 15(5), 491–501. https://doi.org/10.1016/j.trf.2012.05.004.

    Article  Google Scholar 

19. Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science, 330(6006), 932–932. https://doi.org/10.1126/science.1192439.

    Article  Google Scholar 

20. Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: The Guilford Press.

    MATH  Google Scholar 

21. Lajunen, T., & Summala, H. (2003). Can we trust self-reports of driving? Effects of impression management on driver behaviour questionnaire responses Transportation research part F: traffic psychology and behaviour, 6, 97–107. https://doi.org/10.1016/S1369-8478(03)00008-1.

    Article  Google Scholar 

22. Lansdown, T. C., Stephens, A. N., & Walker, G. H. (2015). Multiple driver distractions: A systemic transport problem. Accid Anal Prev, 74, 360–367. https://doi.org/10.1016/j.aap.2014.07.006.

    Article  Google Scholar 

23. Lei, M., & Lomax, R. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Struct Equ Model Multidiscip J, 12(1), 1–27. https://doi.org/10.1207/s15328007sem1201_1.

    Article  MathSciNet  Google Scholar 

24. Lemercier, C., Pêcher, C., Berthié, G., Valéry, B., Vidal, V., Paubel, P. V., Cour, M., Fort, A., Galera, C., Gabaude, C., Lagarde, E., & Maury, B. (2014). Inattention behind the wheel: How factual internal thoughts impact attentional control while driving. Saf Sci, 62, 279–285. https://doi.org/10.1016/j.ssci.2013.08.011.

    Article  Google Scholar 

25. Liu, Z. F., Fu, R., & Lan, Z. (2016). The Comparison between Visual Distraction and Cognitive Distraction. In COTA International Conference of Transportation Professionals 2016 (pp. 1525–1535). https://doi.org/10.1061/9780784479896.139.

26. Martens, M. H., & Brouwer, R. F. T. (2013). Measuring being lost in thought: An exploratory driving simulator study. Transport Res F: Traffic Psychol Behav, 20, 17–28. https://doi.org/10.1016/j.trf.2013.04.002.

    Article  Google Scholar 

27. McEvoy, S. P., Stevenson, M. R., & Woodward, M. (2006). The impact of driver distraction on road safety: Results from a representative survey in two Australian states. Injury Prevention, 12(4), 242–247. https://doi.org/10.1136/ip.2006.012336.

    Article  Google Scholar 

28. Missokefalou, E., & Eliou, N. (2012). Recording and evaluation procedure of drivers’ distraction: The case of Thessaloniki ring road. Procedia Soc Behav Sci, 48, 3159–3169. https://doi.org/10.1016/j.sbspro.2012.06.1282.

    Article  Google Scholar 

29. Neyens, D. M., & Boyle, L. N. (2007). The effect of distractions on the crash types of teenage drivers. Accid Anal Prev, 39(1), 206–212. https://doi.org/10.1016/j.aap.2006.07.004.

    Article  Google Scholar 

30. Neyens, D. M., & Boyle, L. N. (2008). The influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers. Accid Anal Prev, 40(1), 254–259. https://doi.org/10.1016/j.aap.2007.06.005.

    Article  Google Scholar 

31. Olson, R. L., Hanowski, R. J., Hickman, J. S., & Bocanegra, J. (2009). Driver distraction in commercial vehicle operations. No. FMCSA-RRT-09-042. Blacksburg, VA: Federal Motor Carrier Safety Administration.

    Google Scholar 

32. Qu, W., Ge, Y., Xiong, Y., Carciofo, R., Zhao, W., & Zhang, K. (2015). The relationship between mind wandering and dangerous driving behavior among Chinese drivers. Saf Sci, 78, 41–48. https://doi.org/10.1016/j.ssci.2015.04.016.

    Article  Google Scholar 

33. Ranney, T. A., Garrott, W. R., & Goodman, M. J. (2001). NHTSA driver distraction research: Past, present, and future. No. 2001-06-0177. SAE technical paper, 2001.

34. Reason, J. T., Manstead, A. S. R., Stradling, S. G., Baxter, J. S., & Campbell, K. (1990). Errors and violations on the road: A real distinction? Ergonomics, 33, 1315–1332.

    Article  Google Scholar 

35. Reed, M. P., & Green, P. A. (1999). Comparison of driving performance on-road and in a low-cost simulator using a concurrent telephone dialling task. Ergonomics, 42(8), 1015–1037. https://doi.org/10.1080/001401399185117.

    Article  Google Scholar 

36. Singh, S. (2015). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. No. DOT HS 812 115 traffic safety facts crash•stats. Washington, DC: National Highway Traffic Safety Administration.

    Google Scholar 

37. Song, X., & Wang, X. (2012). Mind wandering in Chinese daily lives–an experience sampling study. PLoS One, 7(9), e44423. https://doi.org/10.1371/journal.pone.0044423.

    Article  Google Scholar 

38. Stutts, J. C., Reinfurt, D. W., Staplin, L., & Rodgman, E. A. (2001). The role of driver distraction in traffic crashes. Washington D. C: AAA Foundation for Traffic Safety.

    Book  Google Scholar 

39. Tijerina, L., Johnston, S., Parmer, E., Winterbottom, M. D., & Goodman, M. (2000). Driver distraction with wireless telecommunications and route guidance systems. No. HS-809 069. East liberty, OH: Department of Transportation, National Highway Traffic Safety Administration.

    Google Scholar 

40. Treat, J. R., Tumbas, N. S., McDonald, S. T., Shinar, D., Hume, R. D., Mayer, R. E., Stansifer, R. L., & Castellan, N. J. (1979). Tri-level study of the causes of traffic accidents: final report. No. DOT HS 805 099. Bloomington, IN: Institute for Research in Public Safety.

    Google Scholar 

41. Werneke, J., & Vollrath, M. (2012). What does the driver look at? The influence of intersection characteristics on attention allocation and driving behaviour. Accident Analysis & Prevention, 45, 610–619. https://doi.org/10.1016/j.aap.2011.09.048.

    Article  Google Scholar 

42. Wierwille, W. W., Hanowski, R. J., Hankey, J. M., Kieliszewski, C. A., Lee, S. E., Medina, A., & Dingus, T. A. (2002). Identification of driver errors: overview and recommendations. No. FHWA-RD-02-003. United States: Federal Highway Administration.

    Google Scholar 

43. World Health Organization. (2018). Global status report on road safety 2018. Geneva: World Health Organization.

    Book  Google Scholar 

44. Young, K. L., Mitsopoulos-Rubens, E., Rudin-Brown, C. M., & Lenné, M. G. (2012). The effects of using a portable music player on simulated driving performance and task-sharing strategies. Appl Ergon, 43(4), 738–746. https://doi.org/10.1016/j.apergo.2011.11.007.

    Article  Google Scholar 

45. Young, K. L., Salmon, P. M., & Cornelissen, M. (2013). Distraction-induced driving error: An on-road examination of the errors made by distracted and undistracted drivers. Accid Anal Prev, 58, 218–225. https://doi.org/10.1016/j.aap.2012.06.001.

    Article  Google Scholar 

46. Young, K., & Regan, M. (2007). Driver distraction: A review of the literature. In I. J. Faulks, M. Regan, M. Stevenson, J. Brown, A. Porter, & J. D. Irwin (Eds.), Distracted driving (pp. 379–405). Australasian College of Road Safety: Sydney, NSW.

    Google Scholar 

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