Julia Graefe
ABSTRACT
Applications of our everyday devices like smartphones and computers are becoming increasingly smart, adaptive, and personalized. We assume that users will soon expect the same behavior from their cars. Research shows that well-designed adaptivity brings the potential to increase a system's usability and thereby offer a safer driver-vehicle interaction. While car manufacturers already present first interaction concepts, there still seems to be a research gap regarding human factors challenges of adaptivity. In this paper, we will present some of these challenges and explain their relevance within the automotive context. Additionally, we present our research approach for developing recommendations for the design of adaptive user interfaces for in-vehicle comfort and infotainment features.
- Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli. 2018. Trends and Trajectories for Explainable, Accountable and Intelligible Systems. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1-18. DOI=10.1145/3173574.3174156.Google Scholar
Digital Library
- Evgeni Aizenberg and Jeroen van den Hoven. 2020. Designing for human rights in AI. Big Data & Society 7, 2.Google ScholarCross Ref
- Victor Alvarez-Cortes, Victor H. Zárate, Jorge A. Ramirez Uresti, and Benjamin E. Zayas. 2009. Current Challenges and Applications for Adaptive User Interfaces. In Human-Computer Interaction, I. Maurtua, Ed. InTech. DOI=10.5772/7745.Google Scholar
- Angelos Amditis, Luisa Andreone, Aris Polychronopoulos, and Johan Engström. 2005. DESIGN AND DEVELOPMENT OF AN ADAPTIVE INTEGRATED DRIVER-VEHICLE INTERFACE: OVERVIEW OF THE AIDE PROJECT. IFAC Proceedings Volumes 38, 1, 103-108.Google Scholar
- Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019. Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1-13. DOI=10.1145/3290605.3300233.Google ScholarDigital Library
- Apple Inc. 2021. Human Interface Guidelines - Machine Learning. Retrieved June 18, 2021 from https://developer.apple.com/design/human-interface-guidelines/machine-learning/overview/introduction/Google Scholar
- BMW Group 2021. Das neue BMW iDrive. Retrieved June 18, 2021 from https://www.press.bmwgroup.com/deutschland/article/detail/ T0327315DE/das-neue-bmw-idrive?language=deGoogle Scholar
- Amira Dhouib, Abdelwaheb Trabelsi, Christophe Kolski, and Mahmoud Neji. 2016. A classification and comparison of usability evaluation methods for interactive adaptive systems. In 2016 9th International Conference on Human System Interactions (HSI). IEEE, 246-251. DOI=10.1109/HSI.2016.7529639.Google ScholarCross Ref
- Berkeley J. Dietvorst, Joseph P. Simmons, and Cade Massey. 2015. Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of experimental psychology. General 144, 1, 114-126.Google ScholarCross Ref
- Michael C. Dorneich, Kellie A. McGrath, Rachel F. Dudley, and Max D. Morris. 2013. Analysis of the Characteristics of Adaptive Systems. In 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 888-893. DOI=10.1109/SMC.2013.156.Google Scholar
- Mica R. Endsley. 2017. From Here to Autonomy. Human factors 59, 1, 5-27.Google Scholar
- Karen M. Feigh, Michael C. Dorneich, and Caroline C. Hayes. 2012. Toward a characterization of adaptive systems: a framework for researchers and system designers. Human factors 54, 6, 1008-1024.Google Scholar
- Google PAIR. 2019. People + AI Guidebook. Retrieved April 20, 2021 from https://pair.withgoogle.com/guidebook/Google Scholar
- Thilo Hagendorff and Katharina Wezel. 2020. 15 challenges for AI: or what AI (currently) can't do. AI & Soc 35, 2, 355-365.Google ScholarDigital Library
- Melanie Hartmann. 2009. Challenges in Developing User-Adaptive Intelligent User Interfaces. In 17th Workshop on Adaptivity and User Modeling in Interactive Systems, D. Hauger, M. Köck and A. Nauerz, Eds.Google Scholar
- Kristina Höök. 1999. Designing and evaluating intelligent user interfaces. In Proceedings of the 4th international conference on Intelligent user interfaces - IUI '99. ACM Press, New York, New York, USA, 5-6. DOI=10.1145/291080.291082.Google ScholarDigital Library
- Eric Horvitz. 1999. Uncertainty, Action, and Interaction: In Pursuit of Mixed-Initiative Computing. Intelligent Systems, 17-20.Google Scholar
- ISO. 2020. Ergonomics of human-system interaction - Part 110: Interaction principles, 9241-110.Google Scholar
- ISO. 2020. Ergonomics of human-system interaction - Part 210: Human-centred design for interactive systems, 9241-210.Google Scholar
- Anthony Jameson and Krzysztof Z. Gajos. 2012. Systems That Adapt to Their Users. In The human-computer interaction handbook. Fundamentals, evolving technologies, and emerging applications, J. A. Jacko, Ed. Human factors and ergonomics. Taylor & Francis, Boca Raton, 431-455.Google Scholar
- Holger Kussmann, Holger Modler, Johan Engstrom, Anders Agnvall, Paul Piamonte, Gustav Markkula's, Angelos Amditis, Anastasia Bolovinou, Luisa Andreone, Enrica Deregibus, Paul Kompfner, Patrick Robertson, Nuria de Miguel Garcia, Stephane Feron, Harald Berninger, Christophe Couvreur, Thierry Bellet, Johan Scholliers, and Matti Kutila. 2004. Requirements for AIDE HMI and safety functions D3.2.1.Google Scholar
- Henry Lieberman. 2010. User Interface Goals, AI Opportunities. AIMag 30, 4, 16.Google Scholar
- Mark Maybury. 1999. Intelligent user interfaces. In Proceedings of the 4th international conference on Intelligent user interfaces - IUI '99. ACM Press, New York, New York, USA, 3-4. DOI=10.1145/291080.291081.Google ScholarDigital Library
- Jacob Nielsen. 1994. 10 Usability Heuristics for User Interface Design. Retrieved June 21, 2021 from https://www.nngroup.com/articles/ten-usability-heuristics/Google Scholar
- Lena Rittger, Doreen Engelhardt, Oliver Stauch, and Ivo Muth. 2020. Adaptive User Experience und empathische HMI-Konzepte. ATZ 122, 16-21.Google Scholar
- SAE International/ISO. 2021. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, J3016.Google Scholar
- Donghee Shin. 2021. The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies 146.Google Scholar
- Ben Shneiderman. 1997. Direct manipulation for comprehensible, predictable and controllable user interfaces. In Proceedings of the 2nd international conference on Intelligent user interfaces - IUI '97. ACM Press, New York, New York, USA, 33-39. DOI=10.1145/238218.238281.Google ScholarDigital Library
- Ben Shneiderman. 2020. Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human-Computer Interaction 36, 6, 495-504.Google ScholarCross Ref
- Ben Shneiderman, Catherine Plaisant, Maxine S. Cohen, Steven M. Jacobs, and Niklas Elmqvist. 2016. The Eight Golden Rules of Interface Design. Retrieved June 21, 2021 from https://www.cs.umd.edu/users/ben/goldenrules.htmlGoogle Scholar
- Simone Stumpf, Erin Sullivan, Erin Fitzhenry, Ian Oberst, Weng-Keen Wong, and Margaret Burnett, XXStumpf, S., Sullivan, E., Fitzhenry, E., Oberst, I., Wong, W.-K., and Burnett, M. 2008. Integrating rich user feedback into intelligent user interfaces. In Proceedings of the 13th international conference on Intelligent user interfaces - IUI '08. ACM Press, New York, New York, USA, 50. DOI=10.1145/1378773.1378781.Google Scholar
- Hariharan Subramonyam, Colleen Seifert, and Eytan Adar. 2021. ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces. In 26th International Conference on Intelligent User Interfaces. ACM, New York, NY, USA, 48-58. DOI=10.1145/3397481.3450640.Google Scholar
- Patrick Tchankue, Janet Wesson, and Dieter Vogts. 2011. The impact of an adaptive user interface on reducing driver distraction. In Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '11. ACM Press, New York, New York, USA, 87. DOI=10.1145/2381416.2381430.Google ScholarDigital Library
- Alexander Trende, Daniela Gräfing, and Lars Weber. 2019. Personalized user profiles for autonomous vehicles. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings. ACM, New York, NY, USA, 287-291. DOI=10.1145/3349263.3351315.Google ScholarDigital Library
- Sarah Theres Völkel, Christina Schneegass, Malin Eiband, and Daniel Buschek. 2020. What is "intelligent" in intelligent user interfaces? In Proceedings of the 25th International Conference on Intelligent User Interfaces. ACM, New York, NY, USA, 477-487. DOI=10.1145/3377325.3377500.Google ScholarDigital Library
- Marcel Walch, David Lehr, Mark Colley, and Michael Weber. 2019. Don't you see them? In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings. ACM, New York, NY, USA, 232-237. DOI=10.1145/3349263.3351338.Google ScholarDigital Library
- Nadine Walter. 2018. Personalization and context-sensitive user interaction of in-vehicle infotainment systems. PhD Thesis, Technical University of Munich.Google Scholar
- Philipp Wintersberger, Hannah Nicklas, Thomas Martlbauer, Stephan Hammer, and Andreas Riener. 2020. Explainable Automation: Personalized and Adaptive UIs to Foster Trust and Understanding of Driving Automation Systems. In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, New York, NY, USA, 252-261. DOI=10.1145/3409120.3410659.Google ScholarDigital Library
- Austin Wright, Zijie Wang, Haekyu Park, Grace Guo, Fabian Sperrle, Mennatallah El-Assady, Alex Endert, Daniel Keim, Duen Horng Chau. 2020. A Comparative Analysis of Industry Human-AI Interaction Guidelines. ArXiv, abs/2010.11761.Google Scholar
- Qian Yang, Aaron Steinfeld, Carolyn Rosé, and John Zimmerman. 2020. Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1-13. DOI=10.1145/3313831.3376301.Google ScholarDigital Library
- Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy. 2020. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. ACM, New York, NY, USA, 295-305. DOI=10.1145/3351095.3372852.Google Scholar
- What does Well-Designed Adaptivity Mean for Drivers? A Research Approach to Develop Recommendations for Adaptive In-Vehicle User Interfaces that are Understandable, Transparent and Controllable
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