Abstract
Current production cycle cars offer a wide range of driver assistance features spanning from Advanced Driver Assistance Systems to more established systems such as wing mirrors. All these features allow an increasing amount of adaptation enabling the driver to tailor all them to his or her requirements. However, drivers’ usage of and attitude towards these features as well as their possible adaptations are largely unexplored and, as a consequence, not well understood. We present an exploratory survey on this topic and apply an inclusive design approach in order to accommodate the whole range of diversity in our population. The results indicate a low usage rate of driver assistance features as well as their possible adaptations. However, results suggest a high appreciation for a potential smart adaptation of driver assistance features.
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References
Pollard, T.: BMW’s new iDrive OS 7.0: hands-on test. CAR Mag. https://www.carmagazine.co.uk/car-news/tech/bmw-idrive-os-70-what-you-need-to-know-about-the-new-2018-idrive/. Accessed 2 May 2018
Arora, N., Dreze, X., Ghose, A., et al.: Putting one-to-one marketing to work: personalization, customization, and choice. Mark. Lett. 19, 305–321 (2008)
Adcock, I.: The future of car interiors: infotainment, AI and touchscreens of tomorrow. CAR Mag. (2018) https://www.carmagazine.co.uk/car-news/tech/the-future-of-car-interiors-multimedia-and-touchscreens/. Accessed 2 May 2018
Caber, N., Langdon, P.M., Clarkson, P.J.: Intelligent driver profiling system for cars – a basic concept. In: Antona, M., Stephanidis, C. (eds.) Universal Access in Human-Computer Interaction. Virtual, Augmented, and Intelligent Environments, pp. 201–213. Springer, Cham (2018)
Kuehn, M., Hummel, T., Bende, J.: Benefit estimation of advanced driver assistance systems for cars derived from real-life accidents. In: ESV Konf. (2009)
Piao, J., McDonald, M.: Advanced driver assistance systems from autonomous to cooperative approach. Transp. Rev. 28, 659–684 (2008)
TNS Opinion & Social: Use of Intelligent Systems in Vehicles. European Commission (2006)
Trübswetter, N., Bengler, K.: Why should I use ADAS? Advanced driver assistance systems and the elderly: knowledge, experience and usage barriers. In: Proceedings of 7th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, University of Iowa, Bolton Landing, New York, USA, pp. 495–501 (2013)
Viktorova, L., Sucha, M.: Drivers’ acceptance of advanced driver assistance systems – what to consider? Int. J. Traffic Transp. Eng. 8, 320–333 (2018)
Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35, 982–1003 (1989)
Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204 (2000)
Planing, P.: Innovation Acceptance. Springer Fachmedien Wiesbaden, Wiesbaden (2014)
Dillon, A., Morris, M.G.: User acceptance of information technology: theories and models. Annu. Rev. Inf. Sci. Technol. 31, 3–32 (1996)
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, 1–10 (1997)
Kyriakidis, M., de Winter, J.C.F., Stanton, N., et al.: A human factors perspective on automated driving. Theor. Issues Ergon. Sci., 1–27 (2017)
Holweg, M.: The evolution of competition in the automotive industry. In: Parry, G., Graves, A. (eds.) Build Order, pp. 13–34. Springer, London (2008)
Bradley, M., Langdon, P.M., Clarkson, P.J.: An inclusive design perspective on automotive HMI trends. In: Antona, M., Stephanidis, C. (eds.) Universal Access in Human-Computer Interaction. Users and Context Diversity, pp. 548–555. Springer, Cham (2016)
Akamatsu, M., Green, P., Bengler, K.: Automotive technology and human factors research: past, present, and future. Int. J. Veh. Technol. 2013, 1–27 (2013)
Bellotti, F., Gloria, A.D., Montanari, R., Dosio, N., Morreale, D.: COMUNICAR: designing a multimedia, context-aware human-machine interface for cars. Cogn. Technol. Work 7, 36–45 (2005)
Piechulla, W., Mayser, C., Gehrke, H., König, W.: Reducing drivers’ mental workload by means of an adaptive man–machine interface. Transp. Res. Part F Traffic Psychol. Behav. 6, 233–248 (2003)
Wright, J., Stafford-Fraser, Q., Mahmoud, M., Robinson, P., Dias, E., Skrypchuk, L.: Intelligent scheduling for in-car notifications. In: 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), Modena, Italy, pp. 1–6. IEEE (2017)
Hancock, P.A., Verwey, W.B.: Fatigue, workload and adaptive driver systems. Accid. Anal. Prev. 29, 495–506 (1997)
Department for Transport: GB Driving Licence Data (2018). https://data.gov.uk/dataset/d0be1ed2-9907-4ec4-b552-c048f6aec16a/gb-driving-licence-data. Accessed 12 Feb 2019
Fricker, R.D.: Sampling methods for online surveys. In: SAGE Handbook of Online Research Methods, 2nd edn., pp. 162–183. SAGE Publications Ltd., London (2017)
Boone, H.N., Boone, D.A.: Analyzing likert data. J. Ext. 50, 1–5 (2012)
Abraham, H., Seppelt, B., Mehler, B., Reimer, B.: What’s in a name: vehicle technology branding & consumer expectations for automation. In: Proceedings of 9th International Conference on Automotive User Interfaces and Interactive Vehicle Applications - (AutomotiveUI 2017), Oldenburg, Germany, pp. 226–234. ACM Press (2017)
McDonald, A.B., McGehee, D.V., Chrysler, S.T., Askelson, N.M., Angell, L.S., Seppelt, B.D.: National survey identifying gaps in consumer knowledge of advanced vehicle safety systems. Transp. Res. Rec. J. Transp. Res. Board 2559, 1–6 (2016)
Reagan, I.J., Cicchino, J.B., Kerfoot, L.B., Weast, R.A.: Crash avoidance and driver assistance technologies – are they used? Transp. Res. Part F Traffic Psychol. Behav. 52, 176–190 (2018)
Blaschke, C., Breyer, F., Färber, B., Freyer, J., Limbacher, R.: Driver distraction based lane-keeping assistance. Transp. Res. Part F Traffic Psychol. Behav. 12, 288–299 (2009)
Chapline, J.F., Ferguson, S.A., Lillis, R.P., Lund, A.K., Williams, A.F.: Neck pain and head restraint position relative to the driver’s head in rear-end collisions. Accid. Anal. Prev. 32, 287–297 (2000)
Farmer, C.M., Wells, J.K., Lund, A.K.: Effects of head restraint and seat redesign on neck injury risk in rear-end crashes. Traffic Inj. Prev. 4, 83–90 (2003)
Lebarbé, M., Potier, P., Baudrit, P., Petit, P., Trosseille, X., Vallancien, G.: Thoracic injury investigation using PMHS in frontal airbag out-of-position situations (2005). https://doi.org/10.4271/2005-22-0015
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Caber, N., Langdon, P., Clarkson, P.J. (2020). Designing Adaptation in Cars: An Exploratory Survey on Drivers’ Usage of ADAS and Car Adaptations. In: Stanton, N. (eds) Advances in Human Factors of Transportation. AHFE 2019. Advances in Intelligent Systems and Computing, vol 964. Springer, Cham. https://doi.org/10.1007/978-3-030-20503-4_9
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DOI: https://doi.org/10.1007/978-3-030-20503-4_9
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