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How Acceptable Is It to Monitor Driver State? Using the UTAUT Model to Analyse Drivers’ Perceptions Towards the System

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Advances in Human Aspects of Transportation (AHFE 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 786))

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Abstract

This study aims to identify preliminary acceptance and usability goals of a Driver State Monitoring (DSM) system. To demonstrate willingness within a user group to employ DSM technology for the task it is designed to support, the acceptance of the system was investigated using Unified Theory of Acceptance and Use of Technology. Furthermore, a list of ten usability goals was composed and ranked by participants. In total, 95 responses were recorded. The sample consisted of participants of mixed gender and age (M = 34.81, SD = 9.32 years). The measurement and structure model of acceptance was appraised using Structural Equation Modelling. Overall, the model accounted for 22% of the variance in intention to use DSM technology. It was found that the Social Influence factor is the only significant predictor of Behaviour Intention to use a DSM system. To conclude, several implications for researchers and developers of DSM systems are suggested.

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References

  1. Frost & Sullivan: Executive Outlook of Health, Wellness, and Wellbeing Technologies in the Global Automotive Industry Volume-driving OEMs Leading the First Wave of Proliferation (2015)

    Google Scholar 

  2. Melnicuk, V., Birrell, S., Crundall, E., Jennings, P.: Towards hybrid driver state monitoring: review, future perspectives and the role of consumer electronics. In: IEEE Intelligent Vehicle Symposium, pp. 19–22 (2016)

    Google Scholar 

  3. Najm, W.G., Stearns, M.D., Howarth, H., Koopmann, J., Hitz, J.: Evaluation of an automotive rear-end collision avoidance system (2006)

    Google Scholar 

  4. Regan, M.A., Mitsopoulos, E., Haworth, N., Young, K.: Acceptability of Vehicle Intelligent Transport Systems to Victorian Car Drivers (2002)

    Google Scholar 

  5. Van Der Laan, J.D., Heino, A., De Waard, D.: A simple procedure for the assessment of acceptance of advanced transport telematics. Transp. Res. Part C Emerg. Technol. 5(1), 1–10 (1997)

    Article  Google Scholar 

  6. Adell, E., Nilsson, L., Varhelyi, A.: How is acceptance measured? Overview of measurement issues, methods and tools. In: Driver Acceptance of New Technology. Theory, Measurement and Optimisation, pp. 73–89. Ashgate Publishing (2014)

    Google Scholar 

  7. Kaul, V., Singh, S., Rajagopalan, K., Coury, M.: Consumer attitudes and perceptions about safety and their preferences and willingness to pay for safety (2010)

    Google Scholar 

  8. Brookhuis, K.A.: Detection, tutoring and enforcement of traffic rules violations - the DETER project. In: Vehicle Navigation and Information Systems Conference, pp. 698–701 (1993)

    Google Scholar 

  9. Venkatesh, V., Morris, M., Davis, G., Davis, F.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)

    Article  Google Scholar 

  10. Adell, E., Varhelyi, A., Nilsson, L.: Modelling acceptance of driver assistance systems: application of the unified theory of acceptance and use of technology. In: Driver Acceptance of New Technology. Theory, Measurement and Optimisation of New Technology, pp. 23–34 (2014)

    Google Scholar 

  11. Madigan, R., Louw, T., Dziennus, M., Merat, N.: Acceptance of automated road transport systems (ARTS): an adaptation of the UTAUT model. In: Proceedings of 6th Transport Research Arena, April, pp. 2217–2226 (2016)

    Google Scholar 

  12. BSI: BS EN ISO 9241-210:2010 Ergonomics of human-system interaction - Part 210: Human-centred design for interactive systems. Int. Stand. Organ., vol. 2010, pp. 1–32 (2010)

    Google Scholar 

  13. BS: BS EN ISO 9241-11:1998 Ergonomic requirements for office work with visual display terminals (VDTs). Guidance on usability. Int. Stand. Organ., vol. 6 (1998)

    Google Scholar 

  14. Regan, M.A., Horberry, T., Stevens, A.: Driver Acceptance of New Technology: Theory, Measurement and Optimisation. CRC Press, Boca Raton (2014)

    Google Scholar 

  15. Hair, J., Black, W., Babin, B., Anderson, R.: Multivariate Data Analysis, 7th edn. Pearson Education Limited, Harlow (2014)

    Google Scholar 

  16. Bagozzi, R.R.: On the evaluation of structural equation models, vol. 16, no. 1 (1988)

    Google Scholar 

  17. Chau, P.Y.K., Hu, P.J.-H.: Information technology acceptance by individual professionals: a model comparison approach. Decis. Sci. 32(4), 699–719 (2001)

    Article  Google Scholar 

  18. Browne, M.W., Cudeck, R.: Alternative ways of assessing model fit. Sociol. Methods Res. 21(2), 230–258 (1992)

    Article  Google Scholar 

  19. Hu, L.T., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6(1), 1–55 (1999)

    Article  Google Scholar 

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Acknowledgments

The support for this work was provided by Engineering & Physical Sciences Research Council (EPSRC) and Jaguar Land Rover (JLR).

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Authors and Affiliations

  1. WMG, University of Warwick, Coventry, CV4 7AL, UK

    Vadim Melnicuk, Stewart Birrell & Paul Jennings

  2. Jaguar Land Rover, Banbury Road, Gaydon, CV35 0RR, UK

    Simon Thompson & Alex Mouzakitis

Authors
  1. Vadim Melnicuk
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  2. Stewart Birrell
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  3. Simon Thompson
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  4. Alex Mouzakitis
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  5. Paul Jennings
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Corresponding author

Correspondence to Vadim Melnicuk .

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Editors and Affiliations

  1. Faculty of Engineering and the Environment, Transportation Research Group, University of Southampton, Southampton, UK

    Neville Stanton

Appendix A: Context of Use

Appendix A: Context of Use

Despite recent advances of driver assistance and in-car safety systems the risk of serious accidents on the roads is still present. Traffic related accidents are forecasted to become the third leading cause of the global deaths and injuries by 2020. It was estimated that up to 94% of road accidents happen due to effect of human error. It was found that impaired mental and physical state makes a driver prone to errors and could lead to dangerous driving.

The automotive research community and some automakers are exploring various ways to minimise drivers’ human errors on roads. One of the methods of predicting a human error occurrence is to detect and analyse drivers’ state, such as driving distraction, workload, fatigue, and emotions. This could provide additional assistance in driving task. The feature that detects and analyses drivers’ state is often referred to as Driver State Monitoring (DSM) system. It was forecasted that DSM systems are expected to become a standard passenger car feature by 2025.

The DSM system estimates driving distraction, workload, fatigue, and emotions by keeping track of various physiological parameters. For example, heart rate was found to be a good predictor of workload, whereas sweatiness can be used in estimating level of stress. Similarly, emotions, such as happiness and anger, can be detected by keeping track of drivers’ facial expressions. Therefore, the DSM system may consist of various devices and sensors that are able to capture drivers’ physiological parameters, such as heart rate, sweatiness, and body temperature in real-time while driving. These physiological data may be captured through sensors embedded into a vehicle e.g., seatbelt, steering wheel, and driver’s seat. A driver might be also wearing some devices that offer physiological assessment, for example smart watches, smart textiles, fitness bands, and other wearable devices and sensors. In addition to physiological sensors DSM may include a driver-facing camera for detecting emotional state.

The DSM system will automatically detect the presence of all Driver State Monitoring devices inside a vehicle before every journey and automatically connect to them. It will automatically start tracking and analysing drivers’ state in real-time. The information about drivers’ state could be used to modify in-vehicle information and safety systems. For example, lane assistance system could be at greater awareness state if a driver is experiencing fatigue or high level of workload detected by DSM system. This could help to bring driver’s state and driving performance back into an optimal condition in a particular situation. The system could also track the direct influence of a driving situation on driver’s state and, hence, try to estimate the severity of any particular event. Driver state could also serve as an indication for some specific driving context e.g., individual physiological patterns could be detected during night time driving or during high traffic density situations therefore, further personalise in-vehicle safety features in accordance to individual physiological and emotional responses during various situations. Finally, in-vehicle information systems could become driver state dependent and present only relevant feedback to a driver. Hence, offer enjoyable driving experience. For example, a vehicle could pick the optimal way to communicate some important information to a driver e.g., choose between auditory, visual, or haptic feedback depending on driver’s current level of attentiveness and workload.

Summary of DSM system features:

  • Uses drivers’ state to assist a driver with driving task.

  • Analyses drivers’ state in real-time while driving.

  • Tracks drivers’ physiological and emotional responses.

  • Estimates drivers’ workload, distraction, and fatigue.

  • Combines data from multiple devices and sensors.

  • Provides truly personalised driving experience.

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Melnicuk, V., Birrell, S., Thompson, S., Mouzakitis, A., Jennings, P. (2019). How Acceptable Is It to Monitor Driver State? Using the UTAUT Model to Analyse Drivers’ Perceptions Towards the System. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2018. Advances in Intelligent Systems and Computing, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-93885-1_53

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  • DOI: https://doi.org/10.1007/978-3-319-93885-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93884-4

  • Online ISBN: 978-3-319-93885-1

  • eBook Packages: EngineeringEngineering (R0)

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