221
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Differed Risk Perception in Manual and Automated Driving: An Empirical Study of Varied Conditions

&
Received 13 Apr 2023, Accepted 17 Oct 2023, Published online: 30 Oct 2023

References

  • Azevedo-Sa, H., Zhao, H., Esterwood, C., Yang, X. J., Tilbury, D. M., & Robert, L. P. (2021). How internal and external risks affect the relationships between trust and driver behavior in automated driving systems. Transportation Research Part C, 123, 102973. https://doi.org/10.1016/j.trc.2021.102973
  • Biondi, F., Alvarez, I., & Jeong, K.-A. (2019). Human–vehicle cooperation in automated driving: A multidisciplinary review and appraisal. International Journal of Human–Computer Interaction, 35(11), 932–946. https://doi.org/10.1080/10447318.2018.1561792
  • Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644
  • Castritius, S.-M., Schubert, P., Dietz, C., Hecht, H., Huestegge, L., Liebherr, M., & Haas, C. T. (2021). Driver situation awareness and perceived sleepiness during truck platoon driving-insights from eye-tracking data. International Journal of Human–Computer Interaction, 37(15), 1467–1477. https://doi.org/10.1080/10447318.2021.1894800
  • Feldhütter, A., Gold, C., Schneider, S., & Bengler, K. (2017). How the duration of automated driving influences take-over performance and gaze behavior. In Advances in ergonomic design of systems, products and processes: Proceedings of the annual meeting of GfA 2016 (309–318). Springer. https://doi.org/10.1007/978-3-662-53305-5_22
  • Greenlee, E. T., DeLucia, P. R., & Newton, D. C. (2018). Driver vigilance in automated vehicles: Hazard detection failures are a matter of time. Human Factors, 60(4), 465–476. https://doi.org/10.1177/0018720818761711
  • Greenlee, E. T., DeLucia, P. R., & Newton, D. C. (2022). Driver vigilance decrement is more severe during automated driving than manual driving. Human Factors, 187208221103922. https://doi.org/10.1177/00187208221103922
  • He, D., Kanaan, D., & Donmez, B. (2021). The effect of driving automation on drivers’ anticipatory glances. Congress of the International Ergonomics Association (pp. 655–663). Springer.
  • He, R., Zhao, X. C., & Yang, Y. (2020). Man-machine shared driving model using risk-response mechanism of human driver. Journal of Jilin University (Engineering and Technology Edition), 51(03), 799–809.
  • He, X., Stapel, J., Wang, M., & Happee, R. (2022). Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles. Transportation Research Part F, 86(5), 178–195. https://doi.org/10.1016/j.trf.2022.02.016
  • Jin, M., Lu, G., Chen, F., & Shi, X. (2020). How driving experience affect trust in automation from level 3 automated vehicles? an experimental analysis [Paper presentation]. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) (pp. 1–6), IEEE. https://doi.org/10.1109/ITSC45102.2020.9294247
  • Kolekar, S., de Winter, J., & Abbink, D. (2020). Human-like driving behaviour emerges from a risk-based driver model. Nature Communications, 11(1), 4850. https://doi.org/10.1038/s41467-020-18353-4
  • Kolekar, S., Petermeijer, B., Boer, E., de Winter, J., & Abbink, D. (2021). A risk field-based metric correlates with driver’s perceived risk in manual and automated driving: A test-track study. Transportation Research Part C, 133(19), 103428. https://doi.org/10.1016/j.trc.2021.103428
  • Kondoh, T., Yamamura, T., Kitazaki, S., Kuge, N., & Boer, E. R. (2008). Identification of visual cues and quantification of drivers’ perception of proximity risk to the lead vehicle in car-following situations. Journal of Mechanical Systems for Transportation and Logistics, 1(2), 170–180. https://doi.org/10.1299/jmtl.1.170
  • Körber, M., Cingel, A., Zimmermann, M., & Bengler, K. (2015). Vigilance decrement and passive fatigue caused by monotony in automated driving. Procedia Manufacturing, 3, 2403–2409. https://doi.org/10.1016/j.promfg.2015.07.499
  • Lu, G., Cheng, B., Lin, Q., & Wang, Y. (2012). Quantitative indicator of homeostatic risk perception in car following. Safety Science, 50(9), 1898–1905. https://doi.org/10.1016/j.ssci.2012.05.007
  • Ma, X., Feng, Z., Zhu, X., & Ma, Z. (2018). Driver risk perception model under critical cut-in scenarios [Paper presentation]. Technical Report, SAE Technical Paper. https://doi.org/10.4271/2018-01-1626
  • McWilliams, T., & Ward, N. (2021). Underload on the road: Measuring vigilance decrements during partially automated driving. Frontiers in Psychology, 12, 631364. https://doi.org/10.3389/fpsyg.2021.631364
  • Mishler, S., & Chen, J. (2023). Boring but demanding: Using secondary tasks to counter the driver vigilance decrement for partially automated driving. Human Factors, 187208231168697. https://doi.org/10.1177/00187208231168697
  • Näätänen, R., & Summala, H. (1976). Road-user behaviour and traffic accidents. North-Holland Publishing Company.
  • Navarro, J., Osiurak, F., Ovigue, M., Charrier, L., & Reynaud, E. (2019). Highly automated driving impact on drivers’ gaze behaviors during a car-following task. International Journal of Human–Computer Interaction, 35(11), 1008–1017. https://doi.org/10.1080/10447318.2018.1561788
  • Oken, B. S., Salinsky, M. C., & Elsas, S. (2006). Vigilance, alertness, or sustained attention: Physiological basis and measurement. Clinical Neurophysiology: official Journal of the International Federation of Clinical Neurophysiology, 117(9), 1885–1901. https://doi.org/10.1016/j.clinph.2006.01.017
  • Ping, P., Sheng, Y., Qin, W., Miyajima, C., & Takeda, K. (2018). Modeling driver risk perception on city roads using deep learning. IEEE Access. 6, 68850–68866. https://doi.org/10.1109/ACCESS.2018.2879887
  • Rudin-Brown, C. M., & Parker, H. A. (2004). Behavioural adaptation to adaptive cruise control (acc): Implications for preventive strategies. Transportation Research Part F, 7(2), 59–76. https://doi.org/10.1016/j.trf.2004.02.001
  • Stapel, J., Mullakkal-Babu, F. A., & Happee, R. (2019). Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving. Transportation Research Part F, 60, 590–605. https://doi.org/10.1016/j.trf.2018.11.006
  • Trimpop, R. M. (1996). Risk homeostasis theory: Problems of the past and promises for the future. Safety Science, 22(1–3), 119–130. https://doi.org/10.1016/0925-7535(96)00010-0
  • Vogelpohl, T., Kühn, M., Hummel, T., & Vollrath, M. (2019). Asleep at the automated wheel-sleepiness and fatigue during highly automated driving. Accident Analysis and Prevention, 126, 70–84. https://doi.org/10.1016/j.aap.2018.03.013
  • Wang, J., Wu, J., & Li, Y. (2015). The driving safety field based on driver-vehicle-road interactions. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2203–2214. https://doi.org/10.1109/TITS.2015.2401837
  • Wang, J., Wu, J., Zheng, X., Ni, D., & Li, K. (2016). Driving safety field theory modeling and its application in pre-collision warning system. Transportation Research Part C, 72, 306–324. https://doi.org/10.1016/j.trc.2016.10.003
  • Warm, J. S., Parasuraman, R., & Matthews, G. (2008). Vigilance requires hard mental work and is stressful. Human Factors, 50(3), 433–441. https://doi.org/10.1518/001872008X312152
  • Wilde, G. J. (1988). Risk homeostasis theory and traffic accidents: Propositions, deductions and discussion of dissension in recent reactions. Ergonomics, 31(4), 441–468. https://doi.org/10.1080/10447318.2023.2175520
  • Zhao, X., He, R., & Wang, J. (2020). How do drivers respond to driving risk during car-following? risk-response driver model and its application in human-like longitudinal control. Accident Analysis and Prevention, 148(4), 105783. https://doi.org/10.1016/j.aap.2020.105783

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.