Assessment of Individual Psychophysiological Characteristics of Vehicle Drivers
https://doi.org/10.35596/1729-7648-2026-24-4-89-97
Abstract
To ensure safe transitions between automated and manual driving in automated vehicles, it is necessary to monitor the driver’s state. Existing research in this area primarily assesses the driver’s current state without predicting their potential behavior in complex driving situations, which could negatively impact the safety of automated vehicles when switching between driving modes. To address this shortcoming, this article proposes a method for assessing relatively stable individual driver psychophysiological characteristics that could serve as predictors of their ability to mobilize internal reserves when making decisions in complex driving situations. The method allows one to determine the numerical values of indicators of such individual psychophysiological characteristics as reaction time, ability to take emergency actions, vigilance, concentration and distribution of attention, perception of speed and distance, as well as risk propensity during manual driving.
About the Authors
V. DubovskyBelarus
Dubovsky Vladimir, Cand. Sci. (Tech.), Leading Researcher
220072, Minsk, Akademicheskaya St., 12
Tel.: +375 17 370-07-49
V. Savchenko
Belarus
Savchenko V., Cand. Sci. (Tech.), Associate Professor, Head of the Research Center “On-Board Control Systems for Mobile Vehicles”
220072, Minsk, Akademicheskaya St., 12
References
1. J3016_202104. SAE On-Road Automated Vehicles Standards Committee. Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. Washington, DC, SAE International.
2. Morales-Alvarez W., Sipele O., Léberon R., Tadjine H. H., Olaverri-Monreal C. (2020) Automated Driving: A Literature Review of the Take-Over Request in Conditional Automation. Electronics. 9. https://dx.doi.org/10.3390/electronics9122087.
3. Allamehzadeh A., de la Parra J. U., Hussein A., Garcia F., Olaverri-Monreal C. (2017) Cost-Efficient Driver State and Road Conditions Monitoring System for Conditional Automation. IEEE Intelligent Vehicles Symposium. (IV), 1497–1502. https://doi:10.1109/IVS.2017.7995921.
4. Biondi F., Coleman J. R., Cooper J. M., Strayer D. L. (2016) Average Heart Rate for Driver Monitoring Systems. International Journal of Human Factors and Ergonomics. 4 (3/4), 282–291. https://doi:10.1504/IJHFE.2016.10004220.
5. Perello-March J. R., Burns C. G., Woodman R., Elliott M. T., Birrell S. A. (2022) Driver State Monitoring: Manipulating Reliability Expectations in Simulated Automated Driving Scenarios. IEEE Transactions on Intelligent Transportation Systems. 23 (6), 5187–5197. https://doi:10.1109/TITS.2021.3050518.
6. Yu D., Park C., Choi H., Kim D., Hwang S. H. (2021) Takeover Safety Analysis with Driver Monitoring Systems and Driver–Vehicle Interfaces in Highly Automated Vehicles. Applied Sciences. 11. https://doi.org/10.3390/app11156685.
7. United Nations Economic Commission for Europe (UNECE). Proposal for a New UN Regulation on Uniform Provisions Concerning the Approval of Vehicles with Regards to Automated Lane Keeping System. (2020). https://unece.org/fileadmin/DAM/trans/doc/2020/wp29grva/GRVA-06-02r4e.pdf.
8. Walch M., Colley M., Weber M. (2019) Driving-Task-Related Human-Machine Interaction in Automated Driving: Towards a Bigger Picture. Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings, Utrecht, Netherlands. Automotive UI ’19. USA, NY, Association for Computing Machinery. 427–433. https://doi.org/10.1145/3349263.3351527.
9. Saito T., Wada T., Sonoda K. (2018) Control Authority Transfer Method for Automated-to-Manual Driving Via a Shared Authority Mode. IEEE Transactions on Intelligent Vehicles. 3 (2), 198–207.
10. Abbink D. A., Mulder M., Boer E. R. (2012) Haptic Shared Control: Smoothly Shifting Control Authority? Cognition, Technology & Work. 14, 19–28. https://doi.org/10.1007/s10111-011-0192-5.
11. Mulder M., Abbink D. A., Boer E. R. (2012) Sharing Control with Haptics: Seamless Driver Support from Manual to Automatic Control. Human Factors. 54 (5), 786–798. https://doi:10.1177/0018720812443984. PMID: 23156623.
12. Lv C., Li Y., Xing Y., Huang C., Cao D., Zhao Y., et al. (2021). Human-Machine Collaboration for Automated Driving Using an Intelligent Two‐Phase Haptic Interface. Advanced Intelligent Systems. 3 (4).
13. Halin A., Verly J. G., Droogenbroeck M. (2021) V. Survey and Synthesis of State of the Art in Driver Monitoring. Sensors. 21. https://doi.org/10.3390/s21165558.
Review
For citations:
Dubovsky V., Savchenko V. Assessment of Individual Psychophysiological Characteristics of Vehicle Drivers. Doklady BGUIR. 2026;24(4):89-97. https://doi.org/10.35596/1729-7648-2026-24-4-89-97
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