Computational Modeling of the Dynamics of Human Trust During Human–Machine Interactions

Hu, Wan-Lin; Akash, Kumar; Reid, Tahira; Jain, Neera

doi:10.1109/thms.2018.2874188

Cited by 69 publications

(34 citation statements)

References 53 publications

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“…A relevant approach for modeling the dynamics of trust is that of Hu et al [17], who developed a linear statespace model for the probability of trust responses within two possible choices: trust or distrust in a virtual obstacle detection system. In addition to developing trust-related dynamic models, researchers have tried to use different psychophysiological signals to estimate trust.…”

Section: Dynamics Of Trust and Trust Estimationmentioning

confidence: 99%

“…In addition to developing trust-related dynamic models, researchers have tried to use different psychophysiological signals to estimate trust. For instance, extending Hu's work [17], Akash et al [1] proposed schemes for controlling users' trust levels, applying electroencephalography and galvanic skin response measurements for trust estimation. However, psychophysiology-based methods suffer from at least two drawbacks.…”

Section: Dynamics Of Trust and Trust Estimationmentioning

confidence: 99%

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Real-Time Estimation of Drivers’ Trust in Automated Driving Systems

Azevedo-Sa

Jayaraman

Esterwood

et al. 2020

Int J of Soc Robotics

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Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of techniques for measuring drivers’ trust in the automated driving system during real-time applications execution. One possible approach for measuring trust is through modeling its dynamics and subsequently applying classical state estimation methods. This paper proposes a framework for modeling the dynamics of drivers’ trust in automated driving systems and also for estimating these varying trust levels. The estimation method integrates sensed behaviors (from the driver) through a Kalman filter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers’ performance on a non-driving-related task. We conducted a study ($$n=80$$ n = 80 ) with a simulated SAE level 3 automated driving system, and analyzed the factors that impacted drivers’ trust in the system. Data from the user study were also used for the identification of the trust model parameters. Results show that the proposed approach was successful in computing trust estimates over successive interactions between the driver and the automated driving system. These results encourage the use of strategies for modeling and estimating trust in automated driving systems. Such trust measurement technique paves a path for the design of trust-aware automated driving systems capable of changing their behaviors to control drivers’ trust levels to mitigate both undertrust and overtrust.

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Section: Dynamics Of Trust and Trust Estimationmentioning

confidence: 99%

Section: Dynamics Of Trust and Trust Estimationmentioning

confidence: 99%

Real-Time Estimation of Drivers’ Trust in Automated Driving Systems

Azevedo-Sa

Jayaraman

Esterwood

et al. 2020

Int J of Soc Robotics

View full text Add to dashboard Cite

show abstract

“…A large number of these models are qualitative models [15,30,40,43] which analyze the factors that affect trust but cannot be used to make quantitative predictions. Some quantitative models, including regression models [14,44] and time-series models of trust [2,27,29,[31][32][33]41], fill this gap but do not account for the probabilistic nature of human behavior.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers have proposed to develop paradigms that anticipate human interaction behaviors-such as trust in automation-and influence humans to make optimal choices about automation use [1,17,29,38]. Pre-requisites for such an approach involve the capability to quantitatively predict human behavior and an algorithm for determining the optimal intervention to influence human behavior.…”

Section: Introductionmentioning

confidence: 99%

Toward Adaptive Trust Calibration for Level 2 Driving Automation

Akash

Jain

Misu

2020

Proceedings of the 2020 International Conference on Multimodal Interaction

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“…Privacy impact and control of advanced forms of synthetic data produced by embodied AI decision assistant have been studied in [52] using a so called Conflict Resolver that is the R&T impact detector of decisions produced by the AI assistant. The trust dynamic of human and AI assistant interactions can be formalized, for example, using an approach [24]. Trust level variation is a probabilistic function of human experience E(n) = 1−K(n), where K(n) is human response on warning as the probability of a miss (ignore warning), or a false alarm (mistrust response).…”

Section: Legalitymentioning

confidence: 99%