As drivers are frequently distracted during conditionally automated driving, how and when to issue takeover requests when it is necessary have become concerns. A good practice is to predict takeover performance of drivers in real time, so that appropriate measures can be taken in corresponding to the predicted results. However, that is difficult in that a lot of factors need to be taken into consideration, especially human-related factors. Among all the factors researched, impact of personality on takeover performance has rarely been researched, which is essential for building a personalized prediction model that involves human drivers. To explore the effect of personality on takeover performance, a driving simulator experiment involving 48 participants and 6 critical takeover scenarios was conducted in this study. The big five personality test was utilized for assessing personality of the participants. Overall, results revealed that different personality traits seemed to affect takeover performance in different aspects, such that extraversion and openness mainly affect takeover time, and neuroticism and agreeableness mainly affect longitudinal and lateral performance, respectively. Moreover, effects of personality are most significant when drivers have gained certain levels of situation awareness. Finally, regarding using of turn signals shortly after takeover requests, it was found that turn signal missing rates were positively related with neuroticism, openness and conscientiousness, respectively, and negatively quadratically related with agreeableness. These results might shed light on the factors we need to take into consideration when considering building a personalized prediction model to predict different aspects of takeover performance of drivers.


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    Titel :

    Impact of Personality on Takeover Time and Maneuvers Shortly After Takeover Request


    Beteiligte:
    Huang, Chao (Autor:in) / Yang, Bo (Autor:in) / Nakano, Kimihiko (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2024


    Format / Umfang :

    2754554 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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