ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2026-26-4-887-889ntv-657Research ArticleКРАТКИЕ СООБЩЕНИЯBRIEF PAPERSАнализ корреляции между состоянием водителя и маневрами транспортного средства: трехфазный метод и экспериментальное исследованиеCorrelation analysis of driver state and vehicle maneuvers: a three-phase approach and experimental studyhttps://orcid.org/0009-0003-8710-4995ШушковаВ. В.ShushkovaV. V.

Шушкова Варвара Владимировна — аспирант

Санкт-Петербург, 197101

Varvara V. Shushkova — PhD Student

Saint Petersburg 197101

shushkova.vv@gmail.comhttps://orcid.org/0000-0001-6503-1447КашевникА. М.KashevnikA. M.

Кашевник Алексей Михайлович — кандидат технических наук, доцент, старший научный сотрудник; доцент

sc 16646119200

Санкт-Петербург, 199178

Санкт-Петербург, 197101

Alexey M. Kashevnik — PhD, Associate Professor, Senior Researcher; Associate Professor

sc 16646119200

Saint Petersburg 197101

Saint Petersburg 199178

kashevnik@itmo.ruУниверситет ИТМОРоссияITMO UniversityRussian FederationСанкт-Петербургский Федеральный исследовательский центр Российской академии наук; Университет ИТМОРоссияSt. Petersburg Federal Research Center of the Russian Academy of Sciences; ITMO UniversityRussian Federation202627082026264887889Copyright © Шушкова В.В., Кашевник А.М., 20262026Шушкова В.В., Кашевник А.М.Shushkova V.V., Kashevnik A.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/657

Исследована связь между эмоциональным состоянием водителя и маневрами транспортного средства. На основе системы глобального позиционирования данных с помощью углов Эйлера–Крылова вычислены угловые скорости и динамически определены границы маневров. Эмоции водителей распознавались по видеозаписям из салона автомобиля. Анализ проведен на 42 водителях (более 300 часов вождения). Предложен метод трехфазного представления: оценка эмоций до, во время и после маневра. По результатам кластерного анализа выявлены типичные паттерны эмоциональных реакций, в том числе нарастание гнева при разворотах и снижение позитивных эмоций во время маневров.

This study investigates the relationship between a driver emotional state and vehicle maneuvers. Angular velocity was estimated from GPS trajectories using Euler angles, and maneuver boundaries were identified dynamically. Driver emotions were recognized from in-cabin video recordings. The analysis was conducted on data collected from 42 drivers, comprising more than 300 hours of driving. A three-phase representation of maneuvers is proposed in which emotional responses are evaluated before, during, and after each maneuver. Cluster analysis revealed several characteristic patterns of emotional responses, including increased anger during U-turns and a decrease in positive emotions while performing driving maneuvers.

состояние водителяэмоции водителяманевр транспортного средствакорреляционный анализтрехфазный метод представленияdriver statedriver emotionsvehicle maneuvercorrelation analysisthree-phase representationИсследования выполнены в рамках бюджетной темы FFZF-2025-0003.This work was funded by the Russian State Research FFZF-2025-0003.ReferencesRosen H.E., Bari I., Paichadze N., Peden M., Khayesi M., Monclus J., et al. Global road safety 2010–18: an analysis of global status reports // Injury. 2025. V. 56. N 6. P. 110266. doi: 10.1016/j.injury.2022.07.030Rosen H.E., Bari I., Paichadze N., Peden M., Khayesi M., Monclus J., et al. Global road safety 2010–18: an analysis of global status reports. Injury, 2025, vol. 56, no. 6, pp. 110266. doi: 10.1016/j.injury.2022.07.030Chen Y., Xie Y., Wang C., Yang L., Zheng N., Wu L. Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data // Analytic Methods in Accident Research. 2025. V. 45. P. 100370. doi: 10.1016/j.amar.2025.100370Chen Y., Xie Y., Wang C., Yang L., Zheng N., Wu L. Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data. Analytic Methods in Accident Research, 2025, vol. 45, pp. 100370. doi: 10.1016/j.amar.2025.100370Halin A., Verly J.G., Van Droogenbroeck M. Survey and synthesis of state of the art in driver monitoring // Sensors. 2021. V. 21. N 16. P. 5558. doi: 10.3390/s21165558Halin A., Verly J.G., Van Droogenbroeck M. Survey and synthesis of state of the art in driver monitoring. Sensors, 2021, vol. 21, no. 16, pp. 5558. doi: 10.3390/s21165558Yang G., Ridgeway C., Miller A., Sarkar A. Comprehensive assessment of artificial intelligence tools for driver monitoring and analyzing safety critical events in vehicles // Sensors. 2024. V. 24. N 8. P. 2478. doi: 10.3390/s24082478Yang G., Ridgeway C., Miller A., Sarkar A. Comprehensive assessment of artificial intelligence tools for driver monitoring and analyzing safety critical events in vehicles. Sensors, 2024, vol. 24, no. 8, pp. 2478. doi: 10.3390/s24082478Othman W., Kashevnik A., Hamoud B., Shilov N. DriverSVT: Smartphone-measured vehicle telemetry data for driver state identification // Data. 2022. V. 7. N 12. P. 181. doi: 10.3390/data7120181Othman W., Kashevnik A., Hamoud B., Shilov N. DriverSVT: Smartphone-measured vehicle telemetry data for driver state identification. Data, 2022, vol. 7, no. 12, pp. 181. doi: 10.3390/data7120181Shushkova V., Kashevnik A., Bakeeva L. Accelerometer and gyroscope synthetic data calculation based on driver smartphone GPS // Proc. of the 36th Conference of Open Innovations Association (FRUCT). 2024. P. 748–755. doi: 10.23919/fruct64283.2024.10749939Shushkova V., Kashevnik A., Bakeeva L. Accelerometer and gyroscope synthetic data calculation based on driver smartphone GPS. Proc. of the 36th Conference of Open Innovations Association (FRUCT). 2024, pp. 748–755. doi: 10.23919/fruct64283.2024.10749939Truong C., Oudre L., Vayatis N. Ruptures: change point detection in Python // arXiv. 2018. arXiv:1801.00826. doi: 10.48550/arXiv.1801.00826Truong C., Oudre L., Vayatis N. Ruptures: change point detection in Python. arXiv, 2018. arXiv:1801.00826. doi: 10.48550/arXiv.1801.00826Ryumina E., Dresvyanskiy D., Karpov A. In search of a robust facial expressions recognition model: A large-scale visual cross-corpus study // Neurocomputing. 2022. V. 514. P. 435–450. doi: 10.1016/j.neucom.2022.10.013Ryumina E., Dresvyanskiy D., Karpov A. In search of a robust facial expressions recognition model: A large-scale visual cross-corpus study. Neurocomputing, 2022, vol. 514, pp. 435–450. doi: 10.1016/j.neucom.2022.10.013

The authors declare that there are no conflicts of interest present.