Real-Time Estimation of Drivers' Trust in Automated Driving Systems
Azevedo-Sa, Hebert; Jayaraman, Suresh; Esterwood, Connor; Yang, XI Jessie; Robert, Lionel + "Jr"; Tilbury, Dawn
2020-09-19
Citation
Azevedo-Sa, H., Jayaraman, S., Esterwood, C., Yang, X. J., Robert, L. P. and Tilbury, D. (2020). Real-Time Estimation of Drivers’ Trust in Automated Driving Systems, International Journal of Social Robotics, (pdf), DOI:10.1007/s12369-020-00694-1
Abstract
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 lter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers' performance on a non-driving-related task (NDRT). We conducted a study (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 identi cation 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.Publisher
International Journal of Social Robotics
Other DOIs
Subjects
Trust miscalibration Intelligent Transportation Systems Social Human-Robot Interaction Kalman filter non-driving-related task Human Factors Human-in-the-Loop Automated Vehicles Automated vehicles trust Automated cars robot trust self driving cars real time trust estimation trustworthy robotics trust miscalibrations overtrusting technology overtrusting automated driving automated driving systems trust miscalibrations undertrusting technology technology trust driver–AV teams context-aware systems driver trust operator trust autonomous cars autonomous driving systems autonomous vehicles advanced driving systems Driving simulation trust-aware automated driving systems undertrust overtrust human robot interaction robot interactions
Types
Article
Metadata
Show full item recordShowing items related by title, author, creator and subject.
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Petersen, Luke; Robert, Lionel + "Jr"; Yang, X. Jessie; Tilbury, Dawn (SAE International Journal of Connected and Autonomous Vehicles, 2019-03-01)
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