No Risk No Trust: Investigating Perceived Risk in Highly Automated Driving
No Risk No Trust: Investigating Perceived Risk in Highly Automated Driving
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没有风险就没有信任：调查高度自动化驾驶中的感知风险
DOI：
10.1145/3342197.3344525
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
B. Walker
中科院分区：
文献类型：
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作者：
Mengyao Li;Brittany E. Holthausen;Rachel E. Stuck;B. Walker
在评估驾驶员对自动化系统的信任时，感知风险是一个不可避免的，但却被低估的组成部分，尤其是在最初的交互过程中。我们设计了两个实验研究，关注人们如何在不同的驾驶环境中评估风险，以及关于自动化可靠性的介绍信息如何影响信任和风险感知。首先，我们设计了9种驾驶场景，以确定影响感知情境风险（PSR）和感知关系风险（PRR）的因素。结果表明，参与者根据交通类型和车辆的异常行为识别风险水平。然后，我们评估了介绍性信息和情境风险如何影响信任和PRR。结果表明，当提供有关高可靠性系统的信息以及在低风险情况下驾驶时，参与者报告了最高水平的信任和感知自动化可靠性，以及最低水平的PRR。这些结果强调了整合感知风险和介绍性信息以支持自动车辆信任校准的重要性。
When evaluating drivers' trust in automated systems, perceived risk is an inevitable, yet underestimated component, especially during initial interaction. We designed two experimental studies focusing on how people assess risk in different driving environments and how introductory information about automation reliability influences trust and risk perception. First, we designed nine driving scenarios to determine which factors influence Perceived Situational Risk (PSR) and Perceived Relational Risk (PRR). Results showed that participants identified levels of risk based on traffic type and vehicles' abnormal behaviors. We then evaluated how introductory information and situational risk influence trust and PRR. Results showed that participants reported the highest level of trust, perceived automation reliability, and the lowest level of PRR when presented with information about a highly reliable system, and when driving in a low-risk situation. These results highlight the importance of incorporating perceived risk and introductory information to support the trust calibration in automated vehicles.