Personality Openness Predicts Driver Trust in Automated Driving

Li, Wenmin; Yao, Nailang; Shi, Yue; Nie, Weiran; Zhang, Yuhai; Li, Xiangrong; Liang, Jiawen; Chen, Fang; Gao, Zaifeng

doi:10.1007/s42154-019-00086-w

Cited by 29 publications

(15 citation statements)

References 32 publications

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“…On one hand, several studies investigated trust regarding specific AI-technologies. Li et al ( 2020 ) consistently observed correlations between openness and trust in automated driving. While the participants showed lower trust in automated driving with higher openness in a questionnaire, they also showed a higher monitoring frequency, more frequent, and earlier and longer “take overs” in an automated driving simulator.…”

Section: Outlook On Distributed Cognition In An Ai-enhanced Futurementioning

confidence: 94%

“…Individuals high in openness might strive for more intellectually demanding tasks and thus do not heavily rely on automated systems (but see Zhang et al, 2020 , for conflicting results). Additionally, higher extraversion was related to less trust in the questionnaire but no other effects were observed (Li et al, 2020 ). In contrast, Kraus et al ( 2021 ) did not observe a relationship between openness and trust in automated driving, but indirect effects of neuroticism, extraversion, and agreeableness.…”

Section: Outlook On Distributed Cognition In An Ai-enhanced Futurementioning

confidence: 99%

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Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future

Grinschgl

Neubauer

2022

Front. Artif. Intell.

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In the present article, we explore prospects for using artificial intelligence (AI) to distribute cognition via cognitive offloading (i.e., to delegate thinking tasks to AI-technologies). Modern technologies for cognitive support are rapidly developing and increasingly popular. Today, many individuals heavily rely on their smartphones or other technical gadgets to support their daily life but also their learning and work. For instance, smartphones are used to track and analyze changes in the environment, and to store and continually update relevant information. Thus, individuals can offload (i.e., externalize) information to their smartphones and refresh their knowledge by accessing it. This implies that using modern technologies such as AI empowers users via offloading and enables them to function as always-updated knowledge professionals, so that they can deploy their insights strategically instead of relying on outdated and memorized facts. This AI-supported offloading of cognitive processes also saves individuals' internal cognitive resources by distributing the task demands into their environment. In this article, we provide (1) an overview of empirical findings on cognitive offloading and (2) an outlook on how individuals' offloading behavior might change in an AI-enhanced future. More specifically, we first discuss determinants of offloading such as the design of technical tools and links to metacognition. Furthermore, we discuss benefits and risks of cognitive offloading. While offloading improves immediate task performance, it might also be a threat for users' cognitive abilities. Following this, we provide a perspective on whether individuals will make heavier use of AI-technologies for offloading in the future and how this might affect their cognition. On one hand, individuals might heavily rely on easily accessible AI-technologies which in return might diminish their internal cognition/learning. On the other hand, individuals might aim at enhancing their cognition so that they can keep up with AI-technologies and will not be replaced by them. Finally, we present own data and findings from the literature on the assumption that individuals' personality is a predictor of trust in AI. Trust in modern AI-technologies might be a strong determinant for wider appropriation and dependence on these technologies to distribute cognition and should thus be considered in an AI-enhanced future.

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Section: Outlook On Distributed Cognition In An Ai-enhanced Futurementioning

confidence: 94%

Section: Outlook On Distributed Cognition In An Ai-enhanced Futurementioning

confidence: 99%

Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future

Grinschgl

Neubauer

2022

Front. Artif. Intell.

View full text Add to dashboard Cite

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“…This is typically time-variant, and it can be influenced by the stress, workload, cognition, scenario, and personality of the driver [214,215]; even the educational background and usage experiences can be associated with trust [216,217]. Li et al [218], for example, investigated the relationship between driver personality and driver trust in the AV and discovered a significant correlation between Openness and driver trust. Xing et al [74] summarized all influential factors into three categories: prior, short-term, and long-term factors.…”

Section: F Monitoring Of Driver Trust On Vehicle Automationmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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“…Intelligent agents are now part of our lives and their numbers are expected to increase [ 1 , 2 , 3 ]. Therefore, the smartness, safety, and efficiency of interactions and collaborations between humans and agents should continue to improve [ 4 , 5 , 6 , 7 , 8 , 9 ]. In particular, autonomous driving technology could allow humans to share control with intelligent vehicles [ 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling

Zhang

Xing

et al. 2022

Sensors

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Multi-state driver monitoring is a key technique in building human-centric intelligent driving systems. This paper presents an integrated visual-based multi-state driver monitoring framework that incorporates head rotation, gaze, blinking, and yawning. To solve the challenge of head pose and gaze estimation, this paper proposes a unified network architecture that tackles these estimations as soft classification tasks. A feature decoupling module was developed to decouple the extracted features from different axis domains. Furthermore, a cascade cross-entropy was designed to restrict large deviations during the training phase, which was combined with the other features to form a heterogeneous loss function. In addition, gaze consistency was used to optimize its estimation, which also informed the model architecture design of the gaze estimation task. Finally, the proposed method was verified on several widely used benchmark datasets. Comprehensive experiments were conducted to evaluate the proposed method and the experimental results showed that the proposed method could achieve a state-of-the-art performance compared to other methods.

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Personality Openness Predicts Driver Trust in Automated Driving

Cited by 29 publications

References 32 publications

Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future

Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling

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