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Mixed-Method Design for User Behavior Evaluation of Automated Driver Assistance Systems: An Automotive Industry Case

Published online by Cambridge University Press:  26 July 2019

Julia Orlovska*
Affiliation:
Chalmers University of Technology;
Fjolle Novakazi
Affiliation:
Chalmers University of Technology; VOLVO Car Corporation
Casper Wickman
Affiliation:
Chalmers University of Technology; VOLVO Car Corporation
Rikard Soderberg
Affiliation:
Chalmers University of Technology;
*
Contact: Orlovska, Julia, Chalmers University of Technology Industrial and Material Sciense Sweden, [email protected]

Abstract

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Automotive systems are changing rapidly from purely mechanical to smart, programmable assistants. These systems react and respond to the driving environment and communicate with other subsystems for better driver support and safety. However, instead of supporting, the complexity of such systems can result in a stressful experience for the driver, adding to the workload. Hence, a poorly designed system, from a usability and user experience perspective, can lead to reduced usage or even ignorance of the provided functionalities, especially concerning Adaptive Driver Assistance Systems.

In this paper, the authors propose a combined design approach for user behavior evaluation of such systems. At the core of the design is a mixed methods approach, where objective data, which is automatically collected in vehicles, is augmented with subjective data, which is gathered through in- depth interviews with end-users. The aim of the proposed methodology design is to improve current practices on user behavior evaluation, achieve a deeper understanding of driver's behavior, and improve the validity and rigor of the named results.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Angelini, M., Blasilli, G., Lenti, S. and Santucci, G. (2018), “STEIN: Speeding up Evaluation Activities With a Seamless Testing Environment INtegrator”, In EuroVis.Google Scholar
Atieno, O.P. (2009), “An analysis of the strengths and limitation of qualitative and quantitative research paradigms”, Problems of Education in the 21st Century, Vol. 13 No. 1, pp. 1338.Google Scholar
Bryman, A. (2007), “Barriers to Integrating Quantitative and Qualitative Research”, Journal of Mixed Methods Research, Vol. 1 No. 1, pp. 822. Available at: http://doi.org/10.1177/2345678906290531.Google Scholar
Carta, T., Paternò, F. and de Santana, V.F. (2011), “Web Usability Probe: A Tool for Supporting Remote Usability Evaluation of Web Sites”, Lecture Notes in Computer Science, pp. 349357. Available at: http://doi.org/10.1007/978-3-642-23768-3_29.Google Scholar
Chen, S.W., Fang, C.Y. and Tien, C.T. (2013), “Driving behaviour modelling system based on graph construction”, Transportation Research Part C, Vol. 26, pp. 314330.Google Scholar
Cojocaru, S. (2010), Challenges in using mix methods in evaluation. Postmodern Openings, Vol. 1 No. 3, pp. 3547.Google Scholar
Creswell, J.W. (2014), Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.), Sage publications.Google Scholar
Creswell, J.W. and Clark, V.L. (2018), Designing and Conducting Mixed Methods Research (3rd ed.), Sage publications.Google Scholar
Denzin, N.K. and Lincoln, Y.S. (2005), The Sage handbook of qualitative research, Sage Publications Ltd.Google Scholar
Elander, J., West, R. and French, D. (1993), “Behavioral correlates of individual differences in road-traffic crash risk: an examination method and findings”, Psychological Bullettin, Vol. 113, pp. 279294.Google Scholar
Greene, J.C. (2007), Mixed methods in social inquiry (Vol. 9), John Wiley & Sons.Google Scholar
Hollnagel, E., Nabo, A. and Lau, I.V. (2003), “A systemic model for driver-in-control”, International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design.Google Scholar
Ivory, M.Y. and Hearst, M.A. (2001), “The state of the art in automating usability evaluation of user interfaces”, ACM Computing Surveys, Vol. 33 No. 4, pp. 470516. Available at: http://doi.org/10.1145/503112.503114.Google Scholar
Johnson, R.B. and Onwuegbuzie, A.J. (2004), “Mixed Methods Research: A Research Paradigm Whose Time Has Come”, Educational Researcher, Vol. 33 No. 7, pp. 1426. Available at: http://doi.org/10.3102/0013189x033007014.Google Scholar
Merriam, S.B. (2009), Qualitative Research: a guide to design and interpretation, Jos-sey-Bass, San Francisco.Google Scholar
Michon, J.A. (1985), “A critical view of driver behavior models: What do we know, what should we do?”, Human Behavior and Traffic Safety. Plenum Press, New York, pp. 485520.Google Scholar
Naranjo, J.E., Gonzalez, C., Reviejo, J., Garcia, R. and Pedro, T.d. (2003), “Adaptive fuzzy control for inter-vehicle gap keeping”, In IEEE Trans. Intell. Transport. Syst, Vol. 4 No. 3, pp. 132142. http://doi.org/10.1109/TITS.2003.821294.Google Scholar
Orlovska, J., Wickman, C. and Söderberg, R. (2018), “BIG DATA ANALYSIS AS A NEW APPROACH FOR USABILITY ATTRIBUTES EVALUATION OF USER INTERFACES: AN AUTOMOTIVE INDUSTRY CONTEXT”, Proceedings of the DESIGN 2018 15th International Design Conference. Available at: http://doi.org/10.21278/idc.2018.0243.Google Scholar
Rahman, M.S. (2016), “The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review”, Journal of Education and Learning, Vol. 6 No. 1, p. 102. Available at: http://doi.org/10.5539/jel.v6n1p102.Google Scholar
Rovai, A.P., Baker, J.D. and Ponton, M.K. (2013), Social science research design and statistics: A practitioner's guide to research methods and IBM SPSS, Watertree Press LLC.Google Scholar
SAE International (2018), J3016_201806 - Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles.Google Scholar
Sallee, M.W. and Flood, J.T. (2012), “Using Qualitative Research to Bridge Research, Policy, and Practice”, Theory Into Practice, Vol. 51 No. 2, pp. 137144. Available at: http://doi.org/10.1080/00405841.2012.662873.Google Scholar
Skyttner, L. (1996), General Systems Theory: An introduction, Macmillan Publishers Limited, London, UK. https://doi.org/10.1007/978-1-349-13532-5.Google Scholar
Tornell, S., et al. (2015), “Simplifying the in-vehicle connectivity for ITS applications”, Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. Available at: http://doi.org/10.4108/eai.22-7-2015.2260058.Google Scholar