Adaptive User Experience in the Car-Levels of Adaptivity and Adaptive HMI Design

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 5; pp. 4866 - 4876
• Main Authors: Rittger, Lena, Engelhardt, Doreen, Schwartz, Robert
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive systems; affective computing; Artificial intelligence; Automation; Automobiles; Guidelines; human–computer interaction; Intelligent systems; intelligent vehicles; interactive systems; Perception; road vehicles; Systems analysis; Task analysis; User experience; User groups; User interfaces; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3124990

Advancements in driver state detection and artificial intelligence allow for more and more user-centred and individual experiences. Intelligence and adaptivity in the vehicle context address the three main goals: Increasing safety, usability and empathy in vehicle systems. Adaptivity of systems can be evaluated by considering the technical system features, user-interface-related features and the actual user experience of the adaptive system. We provide an overview of classifications for adaptive systems including the Levels of Adaptive Sensitive Responses (LASR). The levels differentiate the input that a system considers in its operations to adapt to user groups or to the individual user. Along with that, we propose User Experience (UX) design guidelines applicable to the different levels. In an online survey, we varied LASR and one of the UX design guidelines, namely transparency. The within-subjects study showed that both, the levels and the variation of transparency, influenced the perception of intelligence, transparency and intuitive design. However, a significant proportion of users did not understand the difference between the two LASR versions, indicating that users build mental models of systems that imply more personal data usage than the system actually employs. The LASR framework allowed this differentiation to be revealed in system performance and user perception. More research is necessary to elaborate the correlation between levels of adaptivity, UX design, specific UX design guidelines and user experience measures.