A Take-Over Request (TOR) is perceived as an unexpected situation by a driver who relies on autonomous mode, causing negative emotions and affecting the driver’s trust in the autonomous vehicles (AVs). To improve the driver’s trust in autonomous mode, we derived the categories of information needed to resolve negative emotions, considering the driver’s confidence and the driving context in the TOR events. To this end, we created a user journey map, a qualitative UX methodology based on the level of trust in autonomous vehicles among a group of mobility UX experts. We subdivided the takeover situations into four sequences and categorized the information derived from each sequence. The results of our study showed a significant increase in anxiety for both groups upon TOR notification, with differences in the information they needed depending on confidence level and sequence. This study is significant because it confirmed differences in drivers’ level of trust in AVs and the types of information they expect from the system by the TOR sequence. It is thus expected that the application of these findings will help design an autonomous driving system that can improve the driver’s trust in conditional AVs.


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    Titel :

    A Study on Types of Autonomous System Information to Improve Driver’s Trust in Takeover Situations


    Weitere Titelangaben:

    Springer ser. in des. and Innovation


    Beteiligte:
    Jin, Sangeun (Herausgeber:in) / Kim, Jeong Ho (Herausgeber:in) / Kong, Yong-Ku (Herausgeber:in) / Park, Jaehyun (Herausgeber:in) / Yun, Myung Hwan (Herausgeber:in) / Joung, Daeun (Autor:in) / Kim, Youngseo (Autor:in) / Han, Jinyoung (Autor:in) / Seol, Yewon (Autor:in)

    Kongress:

    Congress of the International Ergonomics Association ; 2024 ; Jeju, Korea (Democratic People's Republic of) August 25, 2024 - August 29, 2024



    Erscheinungsdatum :

    24.07.2025


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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