Real-Time Estimation of Drivers’ Trust in Automated Driving Systems
Bibliographic Data
| ID | 21199803 |
|---|---|
| Authors | Hebert Azevedo-Sa (0000-0001-7301-6685, University of Michigan, corresponding author), Suresh Kumaar Jayaraman (0000-0002-4874-8582, University of Michigan), Connor T Esterwood (0000-0002-2685-6435, University of Michigan), X Jessie Yang (0009-0000-8251-9268, University of Michigan), Robert L (0000-0002-1410-2601, University of Michigan), Dawn M Tilbury (0000-0002-2510-0556, University of Michigan) |
| Year | 2021 |
| Volume | 13 |
| Issue | 8 |
| Pages | 1911-1927 |
| Publication date | 2021-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Social Robotics (JOURNAL) |
| Journal identifiers | ISSN: 1875-4791 • E-ISSN: 1875-4805 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12369-020-00694-1 |
| OpenAlex | W3087067192 |
| Language | EN |
| Citations received | 8 |
| References cited | 47 |
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
Advanced driver assistance systems · Automotive industry · Computer vision · Data mining · Estimation · Extended Kalman filter · Kalman filter · Machine learning · Systems engineering · Autonomous Vehicle Technology and Safety · Computer Science · Engineering · Healthcare Technology and Patient Monitoring · Human-Automation Interaction and Safety · Artificial Intelligence
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| Citations per year | 2.67 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |