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Trust in automated vehicles: constructs, psychological processes, and assessment
Francesco Walker1, Yannick Forster2, Sebastian Hergeth2
1Cognitive Psychology, Leiden University, Leiden, Netherlands.
Frontiers in Psychology
|December 11, 2023
Summary
Driver trust in automated driving systems is crucial. Experience with diverse situations, including malfunctions, is key to calibrating trust accurately for safe deployment.
Area of Science:
- Human-Computer Interaction
- Automotive Engineering
- Cognitive Psychology
Background:
- Growing research on trust in driving automation systems.
- Need to clarify conceptualization, calibration, and measurement of trust.
- Importance of considering specific levels of driving automation.
Purpose of the Study:
- To clarify how trust in driving automation is conceptualized, calibrated, and measured.
- To identify key factors influencing trust calibration across different automation levels.
- To propose recommendations for improving trust measurement in automated driving research.
Main Methods:
- Literature review and synthesis of existing research on trust in driving automation.
- Analysis of factors influencing trust calibration, including experience and system performance.
- Development of a framework for understanding the dynamics of trust calibration.
Main Results:
- Driver experience, encompassing a wide range of encountered situations and system malfunctions, is vital for trust calibration.
- Current methods for quantifying trust often lack objectivity, reliability, and validity.
- A framework is proposed to describe the dynamics of trust calibration.
Conclusions:
- Safe deployment of automated vehicles hinges on drivers developing appropriate trust levels.
- Drivers must mentally model system capabilities, including potential unexpected situations.
- Developing methods to foster accurate trust calibration is critically important.
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