Clustering Human Trust Dynamics for Customized Real-Time Prediction

Liu, Jundi; Akash, Kumar; Misu, Teruhisa; Wu, Xingwei

doi:10.1109/itsc48978.2021.9565016

Cited by 7 publications

(9 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consistent with this pattern, subjective ratings of trust of these drivers show that these drivers trusted the reliable automation more. These results are consistent with studies that have found differences in trust dynamics across people that lead to bimodal distributions (Bhat et al, 2022; Liu et al, 2021). A focus on strategies to calibrate trust in drivers with high trust in automation could prove useful in managing their reliance on automation.…”

Section: Discussionsupporting

confidence: 92%

Bimodal Trust: High and Low Trust in Vehicle Automation Influence Response to Automation Errors

Kamaraj,

Lee,

Parker

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

Extended exposure to reliable automation may lead to overreliance as evidenced by poor responses to auto-mation errors. Individual differences in trust may also influence responses. We investigated how these factors affect response to automation errors in a driving simulator study comprised of stop-controlled and uncon-trolled intersections. Drivers experienced reliable vehicle automation during six drives where they indicated if they felt the automation was going too slow or too fast by pressing the accelerator or brake pedal. Engage-ment via pedal presses did not affect the automation but offered an objective measure of trust in automation. In the final drive, an error occurred where the vehicle failed to stop at a stop-controlled intersection. Drivers’ response to the error was inferred from brake presses. Mixture models showed bimodal response times and revealed that drivers with high trust were less likely to respond to automation errors than drivers with low trust.

show abstract

Section: Discussionsupporting

confidence: 92%

Bimodal Trust: High and Low Trust in Vehicle Automation Influence Response to Automation Errors

Kamaraj,

Lee,

Parker

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

show abstract

“…Based on the methods used these models can be divided into two categories: the control theory model and the machine learning model. For the control theory model, the linear time-invariant state-space model was widely used ( 30 , 31 ). For example, Azevedo-Sa et al ( 30 ) used drivers’ behavior (e.g., performance of non-driving-related tasks) and interactive experiences (e.g., system malfunction types and system usage time) to estimate driver trust in AVs by the Kalman filter approach.…”

Section: Trust Estimation and Recognition Model In Automated Vehicle ...mentioning

confidence: 99%

“…For example, Azevedo-Sa et al ( 30 ) used drivers’ behavior (e.g., performance of non-driving-related tasks) and interactive experiences (e.g., system malfunction types and system usage time) to estimate driver trust in AVs by the Kalman filter approach. Liu et al ( 31 ) combined the unsupervised learning approach with the linear time-invariant state-space, establishing the personalized trust estimation model using self-reported ratings and interactive experience data. However, these models have linear assumptions of human trust behaviors.…”

Section: Trust Estimation and Recognition Model In Automated Vehicle ...mentioning

confidence: 99%

Measurement and Real-Time Recognition of Driver Trust in Conditionally Automated Vehicles: Using Multimodal Feature Fusions Network

Cao

Song

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Trust calibration is essential to prevent misuse and disuse of automated vehicles (AVs). Accurate measurement and real-time identification of driver trust is an important prerequisite for achieving trust calibration. Currently, in conditionally automated driving, most researchers utilize self-reported ratings as the ground truth to evaluate driver trust and explore objective trust indicators. However, inconsistencies between the subjective rating and objective behaviors were reported, indicating that trust measurements cannot rely solely on self-reported ratings. To fill this research gap, a method of subjective and objective combination was proposed to measure and identify driver trust in AVs. Thirty-four drivers were involved in a sequence of takeover events. Monitoring ratio and subjective trusting ratings were collected, and combined to measure driver trust levels (i.e., higher and lower trust). Compared with the subjective measurement, the hybrid measurement can more reliably evaluate driver trust in AVs. More importantly, we established a real-time driver trust recognition model for AVs using label smoothing-based convolutional neural network and long short-term memory network fusing multimodal physiological signals (i.e., galvanic skin response and electrocardiogram) and interactive experiences (i.e., takeover-related lead time, takeover frequencies and system usage time). Four common models were developed to compare with the proposed model: Gaussian naive Bayes, support vector machine, convolutional neural network, and long short-term memory network. The comparison results suggest that the performance of our model outperforms others with an F1-score of 75.3% and an area under curve value of 0.812. These findings could have implications for the development of trust monitoring systems in conditionally automated driving.

show abstract

“…To calibrate the levels of driver trust, it is also vital to develop a trust prediction model for AVs. There is some research on the predictive model of driver trust during the non-takeover period in AVs (Azevedo-Sa et al, 2020; Ayoub et al, 2021; Liu et al, 2021). For instance, Azevedo-Sa et al (2020) established the trust model in AVs based on interactive experiences (e.g., system malfunction types and system usage time) and drivers’ behavior (e.g., non-driving-related tasks performance) by the Kalman filter approach, which can estimate driver trust during the non-takeover process.…”

Section: Introductionmentioning

confidence: 99%

How Can the Trust-Change Direction be Measured and Identified During Takeover Transitions in Conditionally Automated Driving? Using Physiological Responses and Takeover-Related Factors

Cao

Song

et al. 2023

Hum Factors

View full text Add to dashboard Cite

Objective This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs). Background Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse. Method 34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers’ physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters. Result Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%. Conclusion The features investigated and the RF model developed can identify trust-change directions during TTs accurately. Application Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers’ overtrust and undertrust in conditionally AVs.

show abstract

Clustering Human Trust Dynamics for Customized Real-Time Prediction

Cited by 7 publications

References 38 publications

Bimodal Trust: High and Low Trust in Vehicle Automation Influence Response to Automation Errors

Bimodal Trust: High and Low Trust in Vehicle Automation Influence Response to Automation Errors

Measurement and Real-Time Recognition of Driver Trust in Conditionally Automated Vehicles: Using Multimodal Feature Fusions Network

How Can the Trust-Change Direction be Measured and Identified During Takeover Transitions in Conditionally Automated Driving? Using Physiological Responses and Takeover-Related Factors

Contact Info

Product

Resources

About