Measuring drivers' physiological response to different vehicle controllers in highly automated driving (HAD): Opportunities for establishing real-time values of driver discomfort

被引:0
作者
Radhakrishnan V. [1 ]
Merat N. [1 ]
Louw T. [1 ]
Lenné M.G. [2 ]
Romano R. [1 ]
Paschalidis E. [1 ]
Hajiseyedjavadi F. [1 ]
Wei C. [1 ]
Boer E.R. [3 ]
机构
[1] Institute of Transport Studies, University of Leeds, Leeds
[2] Seeing Machines Ltd., Canberra
[3] Entropy Control Inc., San Francisco, 94107, CA
来源
Information (Switzerland) | 2020年 / 11卷 / 08期
基金
英国工程与自然科学研究理事会; “创新英国”项目;
关键词
Discomfort; Driver state; Heart-rate variability (HRV); Highly automated driving (HAD); Psychophysiology; Skin conductance response (SCR);
D O I
10.3390/INFO11080390
中图分类号
学科分类号
摘要
This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict "human-like" driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers. © 2020 by the authors.
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共 43 条
  • [31] Dawson M.E., Schell A.M., Filion D.L., The Electrodermal System, Handbook of Psychophysiology, 4th ed., pp. 159-181, (2016)
  • [32] Foy H.J., Chapman P., Mental workload is reflected in driver behaviour, physiology, eye movements and prefrontal cortex activation, Appl. Ergon., 73, pp. 90-99, (2018)
  • [33] Hajiseyedjavadi F., Merat N., Romano R., Paschalidis E., Boer E., Effect of Environmental and Individual Differences on Subjective Evaluation of Human-Like and Conventional Automated Vehicle Controllers, (2020)
  • [34] Louw T., Hajiseyedjavadi F.H., Jamson H., Romano R., Boer E., Merat N., The Relationship between Sensation Seeking and Speed Choice in Road Environments with Different Levels of Risk, In Proceedings of the Tenth International Driving Symposium on Human Factors in Driver Assessment, pp. 29-35, (2019)
  • [35] Boer E.R., Satisficing Curve Negotiation: Explaining Drivers' Situated Lateral Position Variability, IFAC-PapersOnLine, 49, pp. 183-188, (2016)
  • [36] Laborde S., Mosley E., Thayer J.F., Heart rate variability and cardiac vagal tone in psychophysiological research-Recommendations for experiment planning, data analysis, and data reporting, Front. Psychol., 8, (2017)
  • [37] Tarvainen M.P., Niskanen J.P., Lipponen J.A., Ranta-aho P.O., Karjalainen P.A., Kubios HRV-Heart rate variability analysis software, Comput. Methods Prog. Biomed., 113, pp. 210-220, (2014)
  • [38] Kikhia B., Stavropoulos T.G., Andreadis S., Karvonen N., Kompatsiaris I., Savenstedt S., Pijl M., Melander C., Utilizing a wristband sensor to measure the stress level for people with dementia, Sensors., 16, (2016)
  • [39] Benedek M., Kaernbach C., A continuous measure of phasic electrodermal activity, J. Neurosci. Methods, 190, pp. 80-91, (2010)
  • [40] Blanca M.J., Alarcon R., Arnau J., Bono R., Bendayan R., Datos no normales: ¿es el ANOVA una opción válida?, Psicothema, 29, pp. 552-557, (2017)