注意解释:信息类型和错误类型影响自动驾驶车辆的信任和态势感知

IF 4.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yaohan Ding;Lesong Jia;Na Du
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引用次数: 0

摘要

信任和态势感知(SA)对于自动驾驶汽车(av)的接受度和安全性至关重要。虽然已经研究了不同信息类型的自动驾驶解释,以提高驾驶员的信任和SA,但当自动驾驶犯错误而不触发接管请求时,它们的有效性尚不清楚。本研究探讨了信息类型、错误类型及其交互作用对自动驾驶汽车、自动驾驶汽车驾驶员信任及其关系的影响。我们在一项在线视频研究中招募了300名参与者,采用3(信息类型:为什么,如何,为什么+如何)× 3(错误类型:虚报,错过,正确[没有错误])混合设计。信息如何描述车辆的行为,而为什么信息是指车辆行为的原因。线性混合模型显示,与正确情景相比,误报和漏报与较低的SA相关,但可能是由于不同的原因。与正确的情景相比,误报和漏报都与较低的信任度相关,漏报甚至比误报更低,可能是由于潜在后果的严重程度不同。与“为什么”和“为什么”+“如何”信息相比,“如何”信息通常与较低的SA和较高的假警报过度信任的可能性相关。信任与SA在误报和误报中呈负线性关系,而在正确情景中无相关关系。当自动驾驶汽车犯错误时,为了减少潜在的过度信任和对情况的误解,保持较高的SA至关重要。我们建议在AV解释中包含为什么信息,并部署更不容易出错的AV决策系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Watch Out for Explanations: Information Type and Error Type Affect Trust and Situational Awareness in Automated Vehicles
Trust and situational awareness (SA) are critical for the acceptance and safety of automated vehicles (AVs). While AV explanations with different information types have been studied to enhance drivers' trust and SA, their effectiveness remains unclear when AVs make errors that do not trigger takeover requests. This study investigated the effects of information type, error type, and their interaction on drivers' trust in AVs, SA, and their relationships. We recruited 300 participants in an online video study with a 3 (information type: why, how, why + how) × 3 (error type: false alarm, miss, correct [no error]) mixed design. How information describes the vehicle's action, while why information refers to the reason for the vehicle's action. Linear mixed models showed that false alarms and misses were associated with lower SA compared with correct scenarios, but possibly due to different reasons. Compared with correct scenarios, both false alarms and misses were associated with lower trust, with misses even lower than false alarms, possibly due to the varying severity of potential consequences. Compared with why and why + how information, how information was generally associated with lower SA and a higher potential of overtrust in false alarms. Trust and SA had a negative linear relationship in misses and false alarms, while no correlations were found in correct scenarios. To mitigate potential overtrust and misinterpretation of situations when AVs make errors, it is crucial to maintain higher SA. We recommend including why information in AV explanations and deploying AV decision systems that are less miss-prone.
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来源期刊
IEEE Transactions on Human-Machine Systems
IEEE Transactions on Human-Machine Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
7.10
自引率
11.10%
发文量
136
期刊介绍: The scope of the IEEE Transactions on Human-Machine Systems includes the fields of human machine systems. It covers human systems and human organizational interactions including cognitive ergonomics, system test and evaluation, and human information processing concerns in systems and organizations.
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