Understanding Explanation Content for Cognitive and Affective Trust in Automated Vehicles

Trust is essential for the adoption and success of automated vehicles (AVs); however, many users remain skeptical about transferring control to these technologies. To build trust, AVs can provide explanations about their decisions, but what types of explanations matter most? This research explores how different kinds of AV explanations—describing what the vehicle is doing, explaining why it is doing it, or combining both elements—influence user trust. Through an experiment involving 121 U.S. drivers who viewed simulated driving scenarios, this study examined the effects on cognitive trust (related to perceptions of reliability and competence) and affective trust (related to emotional connection). The results indicate that providing explanations significantly improves cognitive trust compared to offering no explanations at all. More importantly, explanations that include reasons for actions particularly enhance affective trust, helping users feel emotionally connected to AVs. This study highlights the importance of transparent communication in AV design, showing that effective explanations are crucial not only for fostering user understanding but also for developing lasting emotional connections. These insights can guide future AV designs that encourage broader public acceptance and trust in automated driving technologies.

Authors

Institutions

Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2025-08-27
DOI
https://doi.org/10.1177/10711813251366284
Citations
1
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding Explanation Content for Cognitive and Affective Trust in Automated Vehicles

Qiaoning Zhang, X. Jessie Yang, Lionel Robert
1 citations
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Explainable Artificial Intelligence (XAI)
2.48
article

Understanding Explanation Content for Cognitive and Affective Trust in Automated Vehicles

Qiaoning Zhang, X. Jessie Yang, Lionel Robert
article en
1 citations

Abstract

Trust is essential for the adoption and success of automated vehicles (AVs); however, many users remain skeptical about transferring control to these technologies. To build trust, AVs can provide explanations about their decisions, but what types of explanations matter most? This research explores how different kinds of AV explanations—describing what the vehicle is doing, explaining why it is doing it, or combining both elements—influence user trust. Through an experiment involving 121 U.S. drivers who viewed simulated driving scenarios, this study examined the effects on cognitive trust (related to perceptions of reliability and competence) and affective trust (related to emotional connection). The results indicate that providing explanations significantly improves cognitive trust compared to offering no explanations at all. More importantly, explanations that include reasons for actions particularly enhance affective trust, helping users feel emotionally connected to AVs. This study highlights the importance of transparent communication in AV design, showing that effective explanations are crucial not only for fostering user understanding but also for developing lasting emotional connections. These insights can guide future AV designs that encourage broader public acceptance and trust in automated driving technologies.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 69(1)
University of Michigan (US), Arizona State University (US)
Openalex Percentile: Top 9%
Explainable Artificial Intelligence (XAI)
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Understanding Explanation Content for Cognitive and Affective Trust in Automated Vehicles — Qiaoning Zhang, X. Jessie Yang, et al. · Proceedings of the Human Factors and Ergonomics Society Annual Meeting (2025) | TGRS Research Map | TGRS