SavedAdd your journal

Journals / International Journal of Automotive Science and Technology / 2025 / Volume: 9 Issue: 4

Determination of DRT in Collision Avoidance: Evaluation of the Results of Tests with Different Driver Groups in the Driving Simulator

Habib Gürbüz(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0001-5157-6227
Canberk Yüksel(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0001-6533-6101
Hüsameddin Akçay(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0002-5704-670X
Pages
—
Publication Date
2025-12-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that there are significant variations in DRT according to driver characteristics and habits.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The driver reaction time (DRT) of road drivers not only significantly affects driving stability and performance but also causes traffic congestion and accidents, so its detection is important for traffic safety.
📊
Key Findings & Data:In this paper, the effect of driver characteristics and habits (i.e., education, job status, gender, age, experience, health status, and driver habits) on DRT is examined through tests performed in a driving simulator on a randomly selected sample group (49 males and 21 females).
💡
Conclusion & Impact:The results show that there are significant variations in DRT according to driver characteristics and habits.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Driver characteristics and habits · Driver DRT · Driving simulator

Abstract

The driver reaction time (DRT) of road drivers not only significantly affects driving stability and performance but also causes traffic congestion and accidents, so its detection is important for traffic safety. In this paper, the effect of driver characteristics and habits (i.e., education, job status, gender, age, experience, health status, and driver habits) on DRT is examined through tests performed in a driving simulator on a randomly selected sample group (49 males and 21 females). Before the tests, 14 different sub-sample groups were created by identifying the characteristics and habits of the drivers based on their own statements through a survey conducted with the sample group. From the DRTs determined for each subsample group, the average DRT and deviation of male and female drivers were calculated separately. The results show that there are significant variations in DRT according to driver characteristics and habits. The DRT of female drivers is approximately 22% higher than that of male drivers. The DRT increases as the driver's age increases. The effect of the driver experience on the DRT is unclear, but the DRT decreases with daily vehicle usage time when considered as an indicator of experience. It was also determined that the DRT becomes shorter as drivers’ educational attainment increases. The DRT is varied by the job status of the drivers; while the unemployed have the highest DRT, public sector employees and students have a significantly lower DRT. In addition, health conditions such as chronic illness, the use of glasses, and fatigue cause a notable increase in DRT. Lifestyle factors such as smoking (negative) and sports activities (positive) affect DRT. While habits such as using mobile phones and CD players/TV while driving negatively affect DRT, seat belt use positively affects DRT.

🔖

Cite This Article

Gürbüz, H., Yüksel, C., Akçay, H. (2025). Determination of DRT in Collision Avoidance: Evaluation of the Results of Tests with Different Driver Groups in the Driving Simulator. International Journal of Automotive Science and Technology, Volume: 9 Issue: 4. https://doi.org/10.30939/ijastech..1680388

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (74)

  1. Yan, M., Chen, W., Wang, J., Zhang, M., Zhao, L. Characteristics and causes of particularly major road traffic accidents involving commercial vehicles in China. Int J Environ Res Public Health. 2021;18(8):3878. https://doi.org/10.3390/ijerph18083878.
  2. Hu, W., Xsiao, X., Xie, D., Tan, T., Maybank, S. Traffic accident prediction using 3-D model-based vehicle tracking. IEEE Technol Soc Mag. 2004;53(3):677-694. https://doi.org/10.1109/TVT. 2004.825772.
  3. Masilkova, M. Health and social consequences of road traffic accidents. Kontakt, 2017;19(1):43-47. https://doi.org/10.1016/j.kontakt.2017.01.007.
  4. De Felice, F., Petrillo, A. Methodological approach for performing human reliability and error analysis in railway transportation system. J Eng Technol. 2011;3(5):341-353. https://api.semanticscholar.org/CorpusID:112826304
  5. Dhillon, B.S., Safety, reliability, human factors, and human error in nuclear power plants. 2019; Ergonomics, 61(11):1569-1570. https://doi.org/10.1080/00140139.2018.1507520
  6. Gürbüz, H., Buyruk, S. Improvement of safe stopping distance and accident risk coefficient based on active driver sight field on real road conditions. IET Intell Transp Syst 2019;13(12):1843-1850. https://doi.org/10.1049/iet-its.2019.0322
  7. Dingus, T.A., Guo, F., Lee, S., Antin, J.F., Perez, M., Buchanan-King, M., Hankey, J. Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc Natl Acad Sci. USA. 2016;113;10:2636-2641. https://doi.org/10.1073/pnas.1513271113
  8. Regan, M.A., Hallett, C., Gordon, C.P. Driver distraction and driver inattention: Definition, relationship and taxonomy. Accid Anal Prev. 2011;43(5):1771-1781. https://doi.org/10.1016/j.aap.2011.04.008
  9. Bragazzi, N.L., Dini, G., Toletone, A., Rahmani, A., Montecucco, A., Massa, E., Durando, P. Patterns of harmful alcohol consumption among truck drivers: implications for occupational health and work safety from a systematic review and meta-analysis. Int. J. Environ. Res Public Health. 2018;15(6):1121. https://doi.org/10.3390/ijerph15061121
  10. Akerstedt, T. Consensus statement: fatigue and accidents in transport operations. J Sleep Res. 2000;9(4):395-395. https://doi.org/10.1046/j.1365-2869.2000.00228.x
  11. Lemke, M.K., Apostolopoulos, Y., Hege, A., Sönmez, S., Wideman, L. Understanding the role of sleep quality and sleep duration in commercial driving safety. Accid Anal Prev. 2016;97:79-86. https://doi.org/10.1016/j.aap.2016.08.024
  12. Useche, S., Cendales, B., Gómez, V. Work stress, fatigue and risk behaviors at the wheel: Data to assess the association between psychosocial work factors and risky driving on Bus Rapid Transit drivers. Data Brief. 2017;15:335-339. https://doi.org/10.1016/j.dib.2017.09.032
  13. Anderson, J.E., Govada, M., Steffen, T.K., Thorne, C.P., Varvarigou, V., Kales, S.N., Burks, S. V. Obesity is associated with the future risk of heavy truck crashes among newly recruited commercial drivers. Accid Anal Prev. 2012;49:378-384. https://doi.org/10.1016/j.aap.2012.02.018
  14. Retting, R.A., Williams, A.F., Greene, M.A. Red-light running and sensible countermeasures: Summary of research findings. Transp Res Rec. 1998;1640(1):23-26. https://doi.org/10.3141/1640-04
  15. Wang, W., Chen, H., Bell, M.C., A study of characteristics of motorway vehicle breakdown duration. Eleventh International Conference on Road Transport Information and Control; 2002; London, UK.
  16. Walker, G.H., Stanton, N.A., Young, M.S. Hierarchical task analysis of driving: a new research tool. Proceedings of the Annual Conference of the Ergonomics Society. Taylor&Francis. 2001:435-440. http://dx.doi.org/10.1201/b12798
  17. Groeger, J.A., Understanding driving: Applying cognitive psychology to a complex everyday task. Routledge; 2000.
  18. Young, M.S., Birrell, S.A., Stanton, N.A. Safe driving in a green world: A review of driver performance benchmarks and technologies to support ‘smart’ driving. Appl Ergon. 2011;42(4):533-539. https://doi.org/10.1016/j.apergo.2010.08.012
  19. Hsiao, H., Chang, J., Simeonov, P. Preventing emergency vehicle crashes: status and challenges of human factors issues. Hum Factors. 2018;60(7):1048-1072. https://doi.org/10.1177/0018720818786132
  20. Kaplan, S., Guvensan, M.A., Yavuz, A.G., Karalurt, Y. Driver behavior analysis for safe driving: A survey. IEEE Trans Intell Transp Syst. 2015;16(6):3017-3032. https://doi.org/10.1109/TITS.2015.2462084
  21. Winkler, S., Kazazi, J., Vollrath, M., Distractive or Supportive -- How Warnings in the Head-up Display Affect Drivers' Gaze and Driving Behavior. IEEE 18th International Conference on Intelligent Transportation Systems;2015;Gran Canaria, Spain.
  22. Fisher, D.L., Rizzo, M., Caird, J.K., Lee, J.D., Handbook of Driving Simulation for Engineering, Medicine, and Psychology. CRC Press;2011.
  23. Liang, Z., Wang, Y., Qian, C., Wang, Y., Zhao, C., Du, H., He, Y. A Driving Simulator Study to Examine the Impact of Visual Distraction Duration from In-Vehicle Displays: Driving Performance, Detection Response, and Mental Workload. Electronics. 2024;13(14):2718. https://doi.org/10.3390/electronics13142718
  24. Voinea, G.D., Boboc, R.G., Buzdugan, I.D., Antonya, C., Yannis, G. Texting while driving: a literature review on driving simulator studies. Int J Environ Res Public Health. 2023;20(5):4354. https://doi.org/10.3390/ijerph20054354
  25. Selander, H., Stave, C., WiIlstrand, T.D., Peters, B. Driving simulator-based training to improve self-rating ability of driving performance in older adults–a pilot study. Eur Transp Res Rev. 2019;11:1-7. https://doi.org/10.1186/s12544-019-0372-6
  26. Lee, M., Kim, S., Kim, J., Yang, J.H. Simulator study on the response time and defensive behavior of drivers in a cut-in situation. Int J Automot Technol. 2022;23(3):817-827. https://doi.org/10.1007/s12239-022-0073-3
  27. Wynne, R. A., Beanland, V., Salmon, P. M. Systematic review of driving simulator validation studies. Saf Sci. 2019;117:138-151. https://doi.org/10.1016/j.ssci.2019.04.004
  28. Medic-Pericevic, S., Mikov, I., Glavaski-Kraljevic, M., Spanovic, M., Bozic, A., Vasovic, V., Mikov, M., 2020. The effects of aging and driving experience on reaction times of professional drivers. Work. 2020;66(2):405-419. https://doi.org/10.3233/wor-203181
  29. Makishita, H., Matsunaga, K. Differences of drivers’ reaction times according to age and mental workload. Accid Anal Prev. 2008;40(2):567-575. http://dx.doi.org/10.1016/j.aap.2007.08.012
  30. Horberry, T., Anderson, J., Regan, M.A., Triggs, T.J., Brown, J. Driver distraction: The effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. Accid Anal Prev. 2006;38(1):185-191. https://doi.org/10.1016/j.aap.2005.09.007
  31. Michaels, J., Chaumillon, R., Nguyen-Tri, D., Watanabe, D., Hirsch, P., Bellavance, F., Giraudet, G., Bernardin, D., Faubert, J., 2017. Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups. Pone. 2017;12(10):e0185909. https://doi.org/10.1371/journal.pone.0185909
  32. Alonso, F., Faus, M., Riera, J.V., Fernandez-Marin, M., Useche, S.A. Effectiveness of driving simulators for drivers’ training: A systematic review. Appl Sci. 2023;13(9):5266. https://doi.org/10.3390/app13095266
  33. Darzi, A., Gaweesh, S.M., Ahmed, M.M., Novak, D. Identifying the causes of drivers’ hazardous states using driver characteristics, vehicle kinematics, and physiological measurements. Front Neurosci. 2018;12:568. https://doi.org/10.3389/fnins.2018.00568
  34. Vieira, F.S., Larocca, A.P.C. Drivers’ speed profile at curves under distraction task. Transp Res Part F Traffic Psychol Behav. 2017;44;12-19. https://doi.org/10.1016/j.trf.2016.10.018
  35. Azimian, A., Catalina Ortega, C.A., Espinosa, J.M., Mariscal, M.Á., García-Herrero, S. Analysis of drivers’ eye movements on roundabouts: A driving simulator study. Sustainability, 2021;13(13):7463. https://doi.org/10.3390/su13137463
  36. Čulík, K., Kalašová, A., Štefancová, V. Evaluation of driver’s reaction time measured in driving simulator. Sensors, 2022;22(9):3542. https://doi.org/10.3390/s22093542
  37. Öztürk, İ., Merat, N., Rowe, R., Fotios, S. The effect of cognitive load on Detection-Response Task (DRT) performance during day-and night-time driving: A driving simulator study with young and older drivers. Transp Res Part F Traffic Psychol Behav. 2023;97:155-169. https://doi.org/10.1016/j.trf.2023.07.002
  38. Han, H., Kim, S., Choi, J., Park, H., Yang, J.H., Kim, J. Driver's avoidance characteristics to hazardous situations: A driving simulator study. Transp Res Part F Traffic Psychol Behav. 2021;81:522-539. https://doi.org/10.1016/j.trf.2021.07.004
  39. Kim, S., Park, S., Jeong, H., Sung, J. Study on response time measurement of distracted driving by virtual reality driving simulator. Int J Adv Comput Res. 2019;9(40):37-45. http://dx.doi.org/10.19101/IJACR.MUL16002
  40. Li, L., Yang, Z., Zeng, J., Carlos, C.Q.J. Evaluating Driver Preferences for In-Vehicle Displays during Distracted Driving Using Driving Simulators. Electronics, 2024;13(8):1428. https://doi.org/10.3390/electronics13081428
  41. Hucho, W.H. Aerodynamics of road vehicles: from fluid mechanics to vehicle engineering. Butterworth-Heinemann, Elsevier;1987.
  42. Milliken, W.F., and D.L. Milliken. Race Car Vehicle Dynamics. SAE International; 2003.
  43. Öz, A., Gürbüz, H., Yakut, A.K., Sağiroğlu, S. Braking performance and noise in excessive worn brake discs coated with HVOF thermal spray process. J Mech Sci Technol. 2017;31:535-543. https://doi.org/10.1007/s12206-017-0106-4
  44. Schubert, T.W., Murteira, C., Collins, E.C., Lopes, D. Scripting RT: A software library for collecting response latencies in online studies of cognition. Pone, 2013;8(6):e67769. https://doi.org/10.1371/journal.pone.0067769
  45. Mohamed, A.A., Mohammed, N.Y., Mahmoud, A. Effect of implementing traffic safety awareness program on Driver’s knowledge regarding traffic safety practices in Alexandria-Egypt Nurs Health Sci. 2018;7(4):39-56. http://dx.doi.org/10.9790/1959-0704113956
  46. Magister, T., Batista, M., Krulec, R., Bogdanovic, L. Measurement of the Driver Response Time in the Simulated and Real Emergency Driving Situations. Promet-Zagreb. 2006;18(1):23-32. https://traffic.fpz.hr/index.php/PROMTT/article/view/660
  47. Rievaj, V., Vrábel, J., Hudák, A. Tire Inflation Pressure Influence on a Vehicle Stopping Distances. Int J Transp Eng.2013;2(2):9-13. http://dx.doi.org/10.5923/j.ijtte.20130202.01
  48. Droz´dziel, P., Tarkowski, S., Rybicka, I., Wrona, R. Drivers’ reaction time research in the conditions in the real traffic. Open Eng. 2020;10(1):35-47. https://doi.org/10.1515/eng-2020-0004
  49. Aksan, N., Anderson, S.W., Dawson, J., Uc, E., Rizzo, M. Cognitive functioning differentially predicts different dimensions of older drivers’ on-road safety. Accid Anal Prev. 2015;75:236-244. https://doi.org/10.1016/j.aap.2014.12.007
  50. Anderson, S.W., Aksan, N., Dawson, J.D., Uc, E.Y., Johnson, A.M., Rizzo, M. Neuropsychological assessment of driving safety risk in older adults with and without neurologic disease. J Clin Exp Neuropsyc. 2012;34(9):895-905. https://doi.org/10.1080/13803395.2011.630654
  51. Fuermaier, A.B.M., Piersma, D., Huntjens, R.J.C., de Waard, D., Westermann, C., Bossert, M., Lange, K.W., Weisbrod, M., Bruggeman, R., Aschenbrenner, S., Tucha, O. Simulated car driving and its association with cognitive abilities in patients with schizophrenia. Schizophr Res. 2019;204:171-177. https://doi.org/10.1016/j. schres.2018.09.005
  52. Morrow, S.A., Classen, S., Monahan, M., Danter, T., Taylor, R., Krasniuk, S., Rosehart, H., He, W. On-road assessment of fitness-to-drive in persons with MS with cognitive impairment: A prospective study. Mult Scler J. 2017;24(11):1499-1506. https://doi.org/10.1177/1352458517723991
  53. Svetina, M. The reaction times of drivers aged 20 to 80 during a divided attention driving. Traffic Inj Prev. 2016;17(8):810-814. https://doi.org/10.1080/15389588.2016.1157590
  54. Hong, J., Kim, J.-W., Chung, H.-Y., Kim, H.-H., Kwon, Y., Kim, C.-S., Ho, Y.-J., Eom, G.-M., Jun, J.-H., Park, B.K. Age–sex differences in the reaction times of ankle muscles. Geriatr Gerontol Int. 2014;14(1):94–99. https://doi.org/10.1111/ggi.12063
  55. Dickerson, A.E., Reistetter, T.A., Burhans, S., Apple, K. Typical brake reaction times across the life span. Occup Ther Health Care. 2016;30(2):115–123. https://doi.org/10.3109/07380577.2015.1059971
  56. Jain, A., Bansal, R., Kumar, A., Singh, K.D. A comparative study of visual and auditory reaction times on the basis of sex and physical activity levels of medical first year students. Int J Appl Basic Med Res., 2015;5(2):124–127. https://doi.org/10.4103/2229-516X.157168
  57. Roivainen, E. Gender differences in processing speed: A review of recent research. Learn Individ Differ. 2011;21(2):145–149. https://doi.org/10.1016/j.lindif.2010.11.021.
  58. Al-Madani, H., Al-Janahi, A.R., Role of drivers’ personal characteristics in understanding traffic sign symbols. Accid Anal Prev. 2002;34(2):185-196. https://doi.org/10.1016/S0001-4575(01)00012-4.
  59. Habekost, T., Vogel, A., Rostrup, E., Bundesen, C., Kyllingsbaek, S., Garde, E., Ryberg, C., Waldemar, G. Visual processing speed in old age. Scand J Psychol. 2013;54:89–94. https://doi.org/10.1111/sjop.12008.
  60. Wickens, C.D., 2002. Multiple resources and performance prediction. Theor Issues Ergon Sci. 2002;3(2):159–177. https://doi.org/10.1080/14639220210123806.
  61. Hancock, P.A., Warm, J.S. A dynamic model of stress and sustained attention. Hum Factors, 1989;31:519–537. https://doi.org/10.7771/2327-2937.1024.
  62. He, J., Chaparro, A., Nguyen, B., Burge, R.J., Crandall, J., Chaparro, B., Ni, R., Cao, S. Texting while driving: Is speech-based text entry less risky than handheld text entry? Accid Anal Prev. 2014;72:287-295. https://doi.org/10.1016/j.aap.2014.07.014.
  63. Törnros, J., Bolling, A. Mobile phone use: Effects of conversation on mental workload and driving speed in rural and urban environments. Transp Res Part F Traffic Psychol Behav. 2006;9:298-306. https://doi.org/10.1016/j.trf.2006.01.008.
  64. Levy, J., Pashler, H., Boer, E. Central interference in driving is there any stopping the psychological refractory period? Psychol. Sci. 2006;17:228–235. https://doi.org/10.1111/j.1467-9280.2006.01690.x.
  65. Otmani, S., Pebayle, T., Roge, J., Muzet, A. Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers. Physiol. Behav. 2005;84(5):715-724. https://doi.org/10.1016/j.physbeh.2005.02.021.
  66. Zuoying, Z., Guixun, Y., Review on driving fatigue detection. Proceedings of the 2022 International Symposium on Sensors, Mechatronics and Automation System;2022;SuZhou, China,
  67. Forbes, W.H., Dill, D.B., DeSilva, H., Van Deventer, F.M. The influence of moderate carbon monoxide poisoning upon the ability to drive automobiles. J Ind Hyg Toxicol.1937;19:598-603.
  68. Stewart, R.D., Peterson, J.E., Baretta, E.D., Bachand, R.T., Hosko, M.J., Herrmann, A.A., 1970. Experimental human exposure to carbon monoxide. Arch Environ Health, 1970;21:154-164. https://doi.org/10.1080/00039896.1970.10667214.
  69. O'Donnell, R.D., Chikosİ P., Theodore, J., 1971. Effect of carbon monoxide exposure on human sleep and psychomotor performance. J Appl Physiol. 1970;31:513-518. https://doi.org/10.1152/jappl.1971.31.4.513.
  70. McFarland, R.A., Roughton, F.J.W., Halperin, M.H., Niven, J.I., 1944. The effects of carbon monoxide and altitude on visual thresholds. J Aviat Med. 1944;15:381-394.
  71. Beard, RR, Wertheim, GA. Behavioral impairment associated with small doses of carbon monoxide. Am J Public Health 1967;57(11):2012-2022.
  72. Schulte, J.H. Effects of mild carbon monoxide intoxication. Arch. Environ. Health. 1963;7(5):524-530.
  73. Horvath, S.M., Dahms, T.E., O'Hanlon, J.F., 1971. Carbon monoxide and human vigilance: A deleterious effect of present urban concentrations. Arch Environ Health. 1971; 23(5):343-347.
  74. Ramsey, J.M., 1972. Carbon monoxide, tissue hypoxia and sensory psychomotor response in hypoxaelnia. Clin Sci. 1972;42(5):619-625.