ERI: Investigate the Role of Affect in Fostering Trust in Automated Driving

ERI：调查情感在培养自动驾驶信任方面的作用

• 批准号: 2138274
• 负责人: Feng Zhou
• 金额: $ 19.99万
• 依托单位: Regents of the University of Michigan - Dearborn
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138274&HistoricalAwards=false
• 关键词: ERI; Investigate; Role; Affect; Fostering

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This Engineering Research Initiation (ERI) grant will support research that will contribute new knowledge related to the role of affect in trust in automated driving, promoting the relationships between human drivers and automated vehicles. The human mind has the affective and cognitive components. So does trust in automation, but affect is the primary determinant of trusting behavior. However, studies of affect on trust in automation are scarce. Therefore, it is critical to examine the role of affect in trust in automated vehicles. The overall objective of the project is to explore the role of affect in trust-based interaction in automated driving. In order to attain the overall objective, three integrated aims will be pursued, i.e., 1) build real-time trust prediction models using psychophysiological measures, 2) investigate the relations between affect and trust in automated vehicles, and 3) calibrate trust in automated vehicles using affect heuristics. Once relevant psychophysiological measures are identified, they can be used to build real-time trust prediction models and once the relationships between the latent structure of affect in trust-based interaction and trust in AVs are identified and the real-time trust prediction model is built, affect can help build and calibrate driver trust in AVs using affect heuristics. Hence, the results from this research will promote the progress of science and national health (e.g., preventing crashes) by advancing our understanding of bidirectional trust-based interaction in automated driving and beyond. First, this research will build real-time trust prediction models using psychophysiological measures, which can overcome the limitations of previous research that mainly measured trust once at the end of a session or twice as pre- and post- levels of trust, which did not reflect the dynamic nature of trust in automation. Second, this research will investigate the relations between affect and trust in automated vehicles by examining the latent structure of affective responses related to trust in automated driving and how affective responses are related to different aspects of trust. Third, this research will examine the role of affect by making use of affect heuristic in automated driving measured by both self-reported and psychophysiological data. Such research endeavors will help understand the role of affect in trust in automated driving.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。这项工程研究启动(ERI)拨款将支持与自动驾驶中情感信任的作用相关的新知识的研究，促进人类驾驶员和自动车辆之间的关系。人类的大脑有情感和认知两个部分。对自动化的信任也是如此，但情感是信任行为的主要决定因素。然而，关于自动化对信任的影响的研究很少。因此，研究情感在自动车辆信任中的作用是至关重要的。该项目的总体目标是探索情感在自动驾驶中基于信任的交互中的作用。为了达到这一总体目标，将追求三个综合目标，即：1)使用心理生理学方法建立实时信任预测模型；2)研究自动车辆中情感与信任之间的关系；3)使用情感启发式方法校准自动车辆中的信任。一旦识别了相关的心理生理测量，它们就可以用来构建实时信任预测模型，一旦识别了基于信任的交互中的情感潜在结构与自动驾驶系统中的信任之间的关系，并建立了实时信任预测模型，影响就可以使用情感启发式方法来帮助建立和校准驾驶员在自动驾驶系统中的信任。因此，这项研究的结果将通过促进我们对自动驾驶和其他领域中基于双向信任的交互的理解，促进科学和国民健康的进步(例如，防止撞车)。首先，本研究将使用心理生理学方法建立实时的信任预测模型，克服了以往研究主要在会话结束时测量一次信任或两次测量信任前后的水平的局限性，没有反映信任在自动化中的动态本质。其次，本研究将通过考察自动驾驶中与信任相关的情感反应的潜在结构，以及情感反应如何与信任的不同方面相关联，来考察自动驾驶中情感与信任之间的关系。第三，本研究将利用情感启发式方法，通过自我报告和心理生理学数据来检验情感在自动驾驶中的作用。这些研究工作将有助于理解情感在自动驾驶信任中的作用。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

使用生理测量的有条件自动驾驶中的实时信任预测

• DOI: 10.1109/tits.2023.3295783
• 发表时间: 2023
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Ayoub, Jackie;Avetisian, Lilit;Yang, X. Jessie;Zhou, Feng
• 通讯作者: Zhou, Feng