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In the Passenger Seat: Differences in the Perception of Human vs. Automated Vehicle Control and Resulting HMI Demands of Users

计算机科学 感知 人机交互 航空学 模拟 控制(管理) 人工智能 心理学 工程类 神经科学
作者
Franziska Hartwich,Cornelia Schmidt,Daniela Gräfing,Josef F. Krems
出处
期刊:Lecture Notes in Computer Science 卷期号:: 31-45 被引量:24
标识
DOI:10.1007/978-3-030-50523-3_3
摘要

With the role change from driver to passenger provoked by driving automation, human factors such as a pleasant driving experience are considered important for a broad system acceptance and usage. However, allocating vehicle control to an automated system has been shown to raise safety concerns among potential users, and to be perceived differently by individuals, e.g. based on their initial trust in the system. To examine both constraints, we compared the effects of human versus fully automated vehicle control on the driving experience (perceived safety, understanding, driving comfort, driving enjoyment) of passengers with lower versus higher trust in driving automation. Additionally, we compared both groups' acceptance of automated driving and system design requirements. From the passenger seat of a driving simulator, 50 participants experienced two randomly ordered drives with identical maneuver execution along an identical test track: a fully automated drive and a manual drive with a human driver. Based on questionnaire ratings before driving, the sample was divided into 26 lower trust participants and 24 higher trust participants. Automated vehicle control was rated as less pleasant than human vehicle control regarding all aspects of driving experience. In addition, perceived safety and driving comfort were positively affected by higher trust. User requirements regarding a pleasant automated driving experience concerned information presentation, but also driving style adjustments. Lower trust participants reported a lower system acceptance and higher need for information during driving than higher trust participants. These results emphasize the importance of a transparent and individually adaptable design of automated driving.
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