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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Springer ser. in des. and Innovation


    Contributors:
    Jin, Sangeun (editor) / Kim, Jeong Ho (editor) / Kong, Yong-Ku (editor) / Park, Jaehyun (editor) / Yun, Myung Hwan (editor) / Joung, Daeun (author) / Kim, Youngseo (author) / Han, Jinyoung (author) / Seol, Yewon (author)

    Conference:

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



    Publication date :

    2025-07-24


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Takeover Level of Autonomous Vehicle Based on Driver’s Neuromuscular Characteristic

    Hanbing, Wei / Yanhong, Wu / Yuxuan, Zhang et al. | British Library Conference Proceedings | 2020



    Getting back in the loop: Does autonomous driving duration affect driver's takeover performance?

    Portron, Arthur / Perrotte, Gaëtan / Ollier, Guillaume et al. | Elsevier | 2024

    Free access

    Trust-oriented HMI Design for Conditional Autonomous Driving Takeover Systems

    Sun, Qi / Hou, Shumeng / Yao, Jie et al. | IEEE | 2022