single-rb.php

JRM Vol.32 No.3 pp. 605-612
doi: 10.20965/jrm.2020.p0605
(2020)

Paper:

Effects of Demographic Characteristics on Trust in Driving Automation

Jieun Lee*, Genya Abe**, Kenji Sato**, and Makoto Itoh*

*University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

**Japanese Automobile Research Institute
2530 Karima, Tsukuba, Ibaraki 305-0822, Japan

Received:
December 20, 2019
Accepted:
April 2, 2020
Published:
June 20, 2020
Keywords:
driving automation, human-machine trust, supervisory control, gender, acceptance of automation
Abstract

With the successful introduction of advanced driver assistance systems, vehicles with driving automation technologies have begun to be released onto the market. Because the role of human drivers during automated driving may be different from the role of drivers with assistance systems, it is important to determine how general users consider such new technologies. The current study has attempted to consider driver trust, which plays a critical role in forming users’ technology acceptance. In a driving simulator experiment, the demographic information of 56 drivers (50% female, 64% student, and 53% daily driver) was analyzed with respect to Lee and Moray’s three dimensions of trust: purpose, process, and performance. The statistical results revealed that female drivers were more likely to rate higher levels of trust than males, and non-student drivers exhibited higher levels of trust than student drivers. However, no driving frequency-related difference was observed. The driver ratings of each trust dimension were neutral to moderate, but purpose-related trust was lower than process- and performance-related trust. Additionally, student drivers exhibited a tendency to distrust automation compared to non-student drivers. The findings present a potential perspective of driver acceptability of current automated vehicles.

Fixed-base driving simulator for partial vehicle automation

Fixed-base driving simulator for partial vehicle automation

Cite this article as:
J. Lee, G. Abe, K. Sato, and M. Itoh, “Effects of Demographic Characteristics on Trust in Driving Automation,” J. Robot. Mechatron., Vol.32 No.3, pp. 605-612, 2020.
Data files:
References
  1. [1] T. L. Mitzner, L. Tiberio, C. C. Kemp, and W. A. Rogers, “Understanding healthcare providers’ perceptions of a personal assistant robot,” Gerontechnology, Vol.17, No.1, pp. 48-55, 2018.
  2. [2] S. Maeso, M. Reza, J. Mayol, J. B. M. Guerra, E. Andradas, and M. Plana, “Efficacy of the Da Vinci Surgical System in Abdominal Surgery Compared with that of Laparoscopy: A Systematic Review and Meta-Analysis,” Ann. Surgery, Vol.252, No.2, pp. 254-262, 2010.
  3. [3] A. Negiz, A. Cinar, J. E. Schlesser, P. Ramanauskas, D. J. Armstrong, and W. Stroup, “Automated control of high temperature short time pasteurization,” Food Control, Vol.7, No.6, pp. 309-315, 1996.
  4. [4] Y. Yamani and W. J. Horrey, “A theoretical model of human-automation interaction grounded in resource allocation policy during automated driving,” Int. J. Hum. Factors Ergon., Vol.5, No.3, pp. 1-15, 2018.
  5. [5] S. Kato, N. Hashimoto, T. Ogitsu, and S. Tsugawa, “Driver Assistance Systems with Communication to Traffic Lights – Configuration of Assistance Systems by Receiving and Transmission and Field Experiments –,” J. Robot. Mechatron., Vol.22, No.6, pp. 737-744, 2010.
  6. [6] Y. Fujinami, P. Raksincharoensak, D. Ulbricht, and R. Adomat, “Risk Predictive Driver Assistance System for Collision Avoidance in Intersection Right Turns,” J. Robot. Mechatron., Vol.30, No.1, pp. 15-23, 2018.
  7. [7] J3016_201806, “Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles,” Warrendale, PA, USA: SAE Int., 2016.
  8. [8] D. J. Fagnant and K. Kockelman, “Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations,” Transp. Res. Part A Policy Pract., Vol.77, pp. 167-181, 2015.
  9. [9] C. Ward, M. Raue, C. Lee, L. D’Ambrosio, and J. F. Coughlin, “Acceptance of automated driving across generations: the role of risk and benefit perception, knowledge, and trust,” M. Kurosu (Ed.) “Human-Computer Interaction. User Interface Design, Development and Multimodality,” HCI 2017, Lecture Notes in Computer Science, Vol.10271, Springer, Cham, pp. 254-266, 2017.
  10. [10] L. M. Hulse, H. Xie, and E. R. Galea, “Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age,” Safety Science, Vol.102, pp. 1-13, 2018.
  11. [11] C. Lee, B. Seppelt, B. Reimer, B. Mehler, and J. F. Coughlin, “Acceptance of Vehicle Automation: Effects of Demographic Traits, Technology Experience and Media Exposure,” Proc. of the Human Factors and Ergonomics Society Annual Meeting, Vol.63, No.1, pp. 2066-2070, 2019.
  12. [12] C. Lee, C. Ward, M. Raue, L. D’Ambrosio, and J. F. Coughlin, “Age differences in acceptance of self-driving cars: a survey of perceptions and attitudes,” J. Zhou and G. Salvendy (Eds.), “Human Aspects of IT for the Aged Population. Aging, Design and User Experience,” ITAP 2017, Lecture Notes in Computer Science, Vol.10297, Springer, Cham, pp. 3-13, 2017.
  13. [13] W. Payre and J. Cestac, “Fully Automated Driving : Impact of Trust and Practice on Manual Control Recovery,” Hum. Factors, Vol.58, No.2, 2013.
  14. [14] C. Rödel, S. Stadler, A. Meschtscherjakov, and M. Tscheligi, “Towards autonomous cars: the effect of autonomy levels on acceptance and user experience,” Proc. of the 6th Int. Conf. on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI’14, New York, NY, USA, pp. 11:1-11:8, 2014.
  15. [15] P. Bansal and K. M. Kockelman, “Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies,” Transportation Research Part A: Policy and Practice, Vol.95, pp. 49-63, 2016.
  16. [16] R. Krueger, T. H. Rashidi, and J. M. Rose, “Preferences for shared autonomous vehicles,” Transp. Res. Part C Emerg. Technol., Vol.69, pp. 343-355, 2016.
  17. [17] P. Bansal, K. M. Kockelman, and A. Singh, “Assessing public opinions of and interest in new vehicle technologies: An Austin perspective,” Transp. Res. Part C Emerg. Technol., Vol.67, pp. 1-14, 2016.
  18. [18] J. D. Lee and K. A. See, “Trust in Automation: Designing for Appropriate Reliance,” Hum. Factors, Vol.46, No.1, pp. 50-80, 2004.
  19. [19] B. M. Muir, “Trust between humans and machines, and the design of decision aids,” Int. J. Man. Mach. Stud., Vol.27, Nos.5-6, pp. 527-539, 1987.
  20. [20] A. Hillary et al., “Autonomous Vehicles and Alternatives to Driving: Trust, Preferences, and Effects of Age,” Proc. of Transportation Research Board 96th Annual Meeting, pp. 1-16, 2017.
  21. [21] T. B. Sheridan, “Trustworthiness of Command and Control Systems,” IFAC Proc. Series, Vol.21, No.3, pp. 427-431, doi: 10.1016/S1474-6670(17)53945-2, 1988.
  22. [22] B. M. Muir, “Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems,” Ergonomics, Vol.37, No.11, pp. 1905-1922, doi: 10.1080/00140139408964957, 1994.
  23. [23] M. Madsen and S. Gregor, “Measuring human-computer trust,” Proc. of the 11th Australasian Conf. on Information Systems, Melbourne, Australia: AIS, pp. 53-64, 2000.
  24. [24] D. Manzey, J. Reichenbach, and L. Onnasch, “Human Performance Consequences of Automated Decision Aids: The Impact of Degree of Automation and System Experience,” J. Cogn. Eng. Decis. Mak., Vol.6, No.1, pp. 57-87, 2012.
  25. [25] B. Muir and N. Moray, “Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation,” Ergonomics, Vol.39, No.3, pp. 429-460, 1996.
  26. [26] M. Körber, E. Baseler, and K. Bengler, “Introduction matters: Manipulating trust in automation and reliance in automated driving,” Applied Ergonomics, Vol.66, pp. 18-31, 2018.
  27. [27] N. Du et al., “Look who’s talking now: Implications of AV’s explanations on driver’s trust, AV preference, anxiety and mental workload,” Transp. Res. Part C Emerg. Technol., Vol.104, pp. 428-442, 2019.
  28. [28] J. Lee and N. Moray, “Trust, Control Strategies and Allocation of Function in Human-Machine Systems,” Ergonomics, Vol.35, No.10, pp. 1243-1270, 1992.
  29. [29] K. Kaur and G. Rampersad, “Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars,” J. Eng. Technol. Manag., Vol.48, pp. 87-96, 2018.
  30. [30] M. König and L. Neumayr, “Users’ resistance towards radical innovations: The case of the self-driving car,” Transp. Res. Part F Traffic Psychol. Behav., Vol.44, pp. 42-52. 2017.
  31. [31] S. Y. Chien, Z. Semnani-Azad, M. Lewis, and K. Sycara, “Towards the development of an inter-cultural scale to measure trust in automation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.8528, pp. 35-46, 2014.
  32. [32] F. D. Davis, “Perceived usefulness, perceived ease of use and user acceptance of information technology,” MIS Quarterly, Vol.13, No.3, pp. 319-340, 1989.
  33. [33] J. Y. Jian, A. M. Bisantz, and C. G. Drury, “Foundations for an empirically determined scale of trust in automated systems,” J. Cogn. Ergon, Vol.4, No.1, pp. 53-71, 2010.
  34. [34] S. Y. Chien, M. Lewis, Z. Semnani-Azad, and K. Sycara, “An empirical model of cultural factors on trust in automation,” Proc. of the Human Factors and Ergonomics Society, Vol.58, No.1, pp. 859-863, 2014.
  35. [35] S. Y. Chien, M. Lewis, S. Hergeth, Z. Semnani-Azad, and K. Sycara, “Cross-country validation of a cultural scale in measuring trust in automation,” Proc. of the Human Factors and Ergonomics Society, Vol.59, No.1, pp. 686-690, 2015.
  36. [36] M. Körber, L. Prasch, and K. Bengler, “Why Do I Have to Drive Now? Post Hoc Explanations of Takeover Requests,” Hum. Factors, Vol.60, No.3, pp. 305-323, 2018.
  37. [37] A. Feldhütter, C. Gold, A. Hüger, and K. Bengler, “Trust in automation as a matter of media influence and experience of automated vehicles,” Proc. of the Human Factors and Ergonomics Society, Vol.60, No.1, pp. 2017-2021, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024