Trust in automated vehicles

Gao, Zaifeng; Li, Wenmin; Liang, Jiawen; Hanxi, Pan; Wang, Xu; Shen, Mowei

doi:10.3724/sp.j.1042.2021.02172

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…There are many studies on the role of anthropomorphism in HMT. Waytz, Heafner, and Epley [99] found that when self‐driving cars have anthropomorphic features [100], such as name and gender, humans are more confident that the vehicle is up to the task of autonomous driving [101]. Thus, the vehicles can be better put into use.…”

Section: Machine Learning and Human‐machine Trust In Healthcarementioning

confidence: 99%

Machine learning and human‐machine trust in healthcare: A systematic survey

Lin,

Han,

et al. 2023

CAAI Trans on Intel Tech

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As human‐machine interaction (HMI) in healthcare continues to evolve, the issue of trust in HMI in healthcare has been raised and explored. It is critical for the development and safety of healthcare that humans have proper trust in medical machines. Intelligent machines that have applied machine learning (ML) technologies continue to penetrate deeper into the medical environment, which also places higher demands on intelligent healthcare. In order to make machines play a role in HMI in healthcare more effectively and make human‐machine cooperation more harmonious, the authors need to build good human‐machine trust (HMT) in healthcare. This article provides a systematic overview of the prominent research on ML and HMT in healthcare. In addition, this study explores and analyses ML and three important factors that influence HMT in healthcare, and then proposes a HMT model in healthcare. Finally, general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

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Section: Machine Learning and Human‐machine Trust In Healthcarementioning

confidence: 99%

Machine learning and human‐machine trust in healthcare: A systematic survey

Lin,

Han,

et al. 2023

CAAI Trans on Intel Tech

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“…Trust is dynamic because it develops as the relationship between the trustor and trustee advances [ 2 , 9 ]. According to the dynamic trust framework [ 10 ], trust in Level 5 ADS develops successively via dispositional, initial, ongoing, and post-task trusts. Dispositional trust reflects a driver’s propensity to trust [ 11 , 12 ], which depends on their culture, age, gender, personality traits [ 12 ], and early trust-related experiences [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Initial trust refers to trust that develops from dispositional trust when the driver has just known about driving automation but has not used it. Therefore, initial trust is influenced by both dispositional trust and drivers’ indirect experiences related to driving automation [ 10 ], such as information acquired from others [ 2 ]. Unlike dispositional and initial trust, ongoing and post-task trust develop by relying on direct contact with driving automation.…”

Section: Introductionmentioning

confidence: 99%

“…Ongoing trust is formed during interactions based on the drivers’ initial trust and perceived real-time characteristics of driving automation. Post-task trust is formed after completing an interaction with driving automation [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…We aimed to focus on initial trust as it played an important role in the connection between dispositional, ongoing, and post-task trust [ 10 , 11 ]. Furthermore, it could affect users’ initial decision of engaging in automation [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of brand and brand trust on initial trust in fully automated driving system

Cui

Itoh

2023

PLoS ONE

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Before Automated Driving Systems (ADS) with full driving automation (SAE Level 5) are placed into practical use, the issue of calibrating drivers’ initial trust in Level 5 ADS to an appropriate degree to avoid inappropriate disuse or improper use should be resolved. This study aimed to identify the factors that affected drivers’ initial trust in Level 5 ADS. We conducted two online surveys. Of these, one explored the effects of automobile brands and drivers’ trust in automobile brands on drivers’ initial trust in Level 5 ADS using a Structural Equation Model (SEM). The other identified drivers’ cognitive structures regarding automobile brands using the Free Word Association Test (FWAT) and summarized the characteristics that resulted in higher initial trust among drivers in Level 5 ADS. The results showed that drivers’ trust in automobile brands positively impacted their initial trust in Level 5 ADS, which showed invariance across gender and age. In addition, the degree of drivers’ initial trust in Level 5 ADS was significantly different across different automobile brands. Furthermore, for automobile brands with higher trust in automobile brands and Level 5 ADS, drivers’ cognitive structures were richer and varied, which included particular characteristics. These findings suggest the necessity of considering the influence of automobile brands on calibrating drivers’ initial trust in driving automation.

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Research on Information Design of Intelligent Vehicle System Interface Based on Driver Trust

2022

Communications in Computer and Information Science

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Trust in automated vehicles

Cited by 12 publications

References 42 publications

Machine learning and human‐machine trust in healthcare: A systematic survey

Machine learning and human‐machine trust in healthcare: A systematic survey

Effects of brand and brand trust on initial trust in fully automated driving system

Research on Information Design of Intelligent Vehicle System Interface Based on Driver Trust

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