Comparative Analysis of Driving Performance and Visual and Physiological Responses Between Professional and Civilian Drivers in Simulated Environments
Source abstract
Abstract
Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human-Automation Interaction and Safety · Traffic and Road Safety · Older Adults Driving Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Vehicle Production and Engineering, Széchenyi István University, H-9026 Győr, Hungary
Institute of the Information Society, Ludovika University of Public Service, H-1083 Budapest, Hungary
Department of Civil & Environmental Engineering, Technion Israel Institute of Technology, Haifa 32000, Israel
Department of Civil Engineering and Architecture, University of Catania, Via S. Sofia, 64, 95123 Catania, Italy
References (37)
Show source-provided references
1. Regan, M.A., Lee, J.D., and Young, K.L. (2009). Driver Distraction Theory, Effects, and Mitigation, CRC Press.10.1201/9781420007497 ↗
2. Regan, M.A., and Hallett, C. (2011). Driver Distraction: Definition, Mechanisms, Effects, and Mitigation, Elsevier.10.1016/B978-0-12-381984-0.10020-7 ↗
3. Minimum Required Attention: A Human-Centered Approach to Driver Inattention10.1177/0018720816672756 ↗
4. Visual Distraction While Driving10.1016/S0386-1112(14)60067-4 ↗
5. Victor, T. (2014). Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk, The National Academies Press.10.17226/22297 ↗
6. (2025, November 04). The Royal Society for the Prevention of Accidents (ROSPA) Road Safety Factsheet: Driver Distraction. Available online: https://www.rospa.com/siteassets/pages/health-and-safety/health-and-safety-news/distracted-driver/distraction-factsheet-0524_1.pdf.
7. Kchour, F., Cafiso, S., and Pappalardo, G. (2025). Understanding Cyclists’ Visual Behavior Using Eye-Tracking Technology: A Systematic Review. Sensors, 25.10.3390/s25010022 ↗
8. Visual Attention While Driving: Sequences of Eye Fixations Made by Experienced and Novice Drivers10.1080/0014013031000090116 ↗
9. Comparative Study of the Effects of Auditory, Visual and Multimodality Displays on Drivers’ Performance in Advanced Traveller Information Systems10.1080/00140130010011369 ↗
10. Nagy, V., Kovács, G., Földesi, P., Kurhan, D., Sysyn, M., Szalai, S., and Fischer, S. (2023). Testing Road Vehicle User Interfaces Concerning the Driver’s Cognitive Load. Infrastructures, 8.10.3390/infrastructures8030049 ↗
11. Evaluating Distraction of In-Vehicle Information Systems While Driving by Predicting Total Eyes-off-Road Times with Keystroke Level Modeling10.1016/j.apergo.2016.04.012 ↗
12. Madlenak, R., Masek, J., Madlenakova, L., and Chinoracky, R. (2023). Eye-Tracking Investigation of the Train Driver’s: A Case Study. Appl. Sci., 13.10.3390/app13042437 ↗
13. The Ability of Eye-Tracking Metrics to Classify and Predict the Perceived Driving Workload10.1016/j.ergon.2021.103193 ↗
14. Evaluating Driver Cognitive Distraction by Eye Tracking: From Simulator to Driving
15. Review of Eye-Related Measures of Drivers’ Mental Workload10.1016/j.promfg.2015.07.783 ↗
16. Mahanama, B., Jayawardana, Y., Rengarajan, S., Jayawardena, G., Chukoskie, L., Snider, J., and Jayarathna, S. (2022). Eye Movement and Pupil Measures: A Review. Front. Comput. Sci., 3.10.3389/fcomp.2021.733531 ↗
17. Distracted Worker: Using Pupil Size and Blink Rate to Detect Cognitive Load during Manufacturing Tasks10.1016/j.apergo.2022.103867 ↗
18. Average Heart Rate for Driver Monitoring Systems10.1504/IJHFE.2016.083521 ↗
19. AI-Driven Driver Behavior Assessment Through Vehicle and Health Monitoring for Safe Driving—A Survey10.1109/ACCESS.2024.3383775 ↗
20. A Dynamic Hybrid Choice Model to Quantify Stress in a Simulated Driving Environment10.1109/TITS.2021.3056146 ↗
21. Physiological Measurements of Situation Awareness: A Systematic Review10.1177/0018720820969071 ↗
22. Driver Distraction Detection Based on Vehicle Dynamics Using Naturalistic Driving Data10.1016/j.trc.2022.103561 ↗
23. Distraction and Related Risk Factors among Professional and Non-Professional Drivers10.1016/j.heliyon.2024.e31530 ↗
24. Impact of Driving Style, Behaviour and Anger on Crash Involvement among Iranian Intercity Bus Drivers10.1016/j.iatssr.2022.07.003 ↗
25. Usage and Perceived Effectiveness of Fatigue Countermeasures for Professional and Nonprofessional Drivers10.1016/j.aap.2010.10.027 ↗
26. The Effects of Driver Training on Simulated Driving Performance10.1016/j.aap.2004.06.005 ↗
27. Skills of Novices Early Trained or Traditionaly Trained versus Experienced Drivers Confronted to Simulated Urban Accidents’ Scenarios
28. Exploring the Sustained and Divided Attention of Novice versus Experienced Drivers
29. Zou, Z., Alnajjar, F., Lwin, M., Ali, L., Al Jassmi, H., Mubin, O., and Swavaf, M. (2025). A Preliminary Simulator Study on Exploring Responses of Drivers to Driving System Reminders on Four Stimuli in Vehicles. Sci. Rep., 15.10.1038/s41598-025-87571-x ↗
30. Measured Increases in Steering Entropy May Predict When Performance Will Degrade: A Driving Simulator Study10.1016/j.trf.2022.10.006 ↗
31. Baumann, C., and Dierkes, K. (2023). Neon Accuracy Test Report—An Evaluation of Lab and In-The-Wild Performance, Pupil Labs.
32. Using a Driving Simulator to Identify Older Drivers at Inflated Risk of Motor Vehicle Crashes10.1016/j.jsr.2003.09.007 ↗
33. Kawabata, K., Nakajima, Y., Fujita, K., Sato, M., Hayashi, K., and Kobayashi, Y. (2024). Pilot Study on Gaze Characteristics of Older Drivers While Watching Driving Movies. Geriatrics, 9.10.3390/geriatrics9050132 ↗
34. Hazard Anticipation of Novice and Experienced Drivers: Empirical Evaluation on a Driving Simulator in Daytime and Nighttime Conditions10.3141/2009-01 ↗
35. Paxion, J., Galy, E., and Berthelon, C. (2014). Mental Workload and Driving. Front. Psychol., 5.10.3389/fpsyg.2014.01344 ↗
36. Envelhecer Altera Relevantemente a Frequência Cardíaca Média?
37. The Recency Effect: Implicit Learning with Explicit Retrieval?10.3758/BF03202726 ↗