Abstract: For years, Level 2 vehicle automation systems (VAS) have been commercially available, yet the extent to which users comprehend their capabilities and limitations remains largely unclear. This study aimed to evaluate user knowledge regarding Level 2 VAS and explore the correlation between user experiences (UX), behavioural adaptations, trust, and acceptance. By using an online survey, we sought to deepen understanding of how UX, trust, and acceptance of Level 2 automated vehicles (AVs) evolve with prolonged use in urban traffic. The survey, comprising demographic data and knowledge inquiries (automated driving experience and timeframes, vehicle operation competency, driving skills over long-term use of automation, the learning process, automation-induced effects, trust in automation, and ADS researchers and manufacturers), was completed by various drivers (N=16). This investigation focused on users' long-term experiences with automation in urban traffic. Consequently, we offer user-centric transformative insights into users' experiences with driving automation in urban traffic settings. Results revealed that users’ knowledge of automation exhibits their learning patterns, trust and acceptance. Moreover, users’ attitudes trust, and acceptance varies across different user profiles. What we have also learned about UX and the changing nature of user behaviours towards automation is that, automated driving changes influence the safety and risk conditions in which users and AVs interact. These findings can inform the development of interaction design strategies and policy aimed at enhancing UX of AV users.

Naomi Y. Mbelekani and Klaus Bengler, “User Experience and Behavioural Adaptation Based on Repeated Usage of Vehicle Automation: Online Survey” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150304

@article{Mbelekani2024,
title = {User Experience and Behavioural Adaptation Based on Repeated Usage of Vehicle Automation: Online Survey},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150304},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150304},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Naomi Y. Mbelekani and Klaus Bengler}
}