Assessing How Driving Self-Efficacy Influences Situational Trust in Driver Assist Technologies

As the movement toward fully autonomous driving continues, it is critical to assess factors that may impact how drivers interact with and perform in conjunction with automated driving systems. The trust drivers place in the driver assist technologies can impact how effectively these systems are util...

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Veröffentlicht in:Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2022-09, Vol.66 (1), p.1260-1264
Hauptverfasser: Ferraro, James C., Mouloua, Mustapha
Format: Artikel
Sprache:eng
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Zusammenfassung:As the movement toward fully autonomous driving continues, it is critical to assess factors that may impact how drivers interact with and perform in conjunction with automated driving systems. The trust drivers place in the driver assist technologies can impact how effectively these systems are utilized. Human factors research has found a variety of factors related to the environment, the system, and the user that influence trust in automated systems. The current study was designed to assess how situational factors influence how drivers trust different driver assist features, and how driving self-efficacy impacts this relationship. 101 participants reported situational trust scores based on 10 vignette traffic scenarios that described driving in high or low complexity situations while using one of five driver assist features. It was hypothesized that situation and automation-related factors would influence participants’ trust. Results indicated a significant interaction between the complexity of the driving scenario and the different driver assist features. Additional results indicated self-efficacy impacted relationships between the driver assist technologies and trust. These findings are consistent with previous results related to trust and use of automation and have implications for researchers interested in how drivers interact with current and emerging driving technologies. These results can be applied to the development of automated driving systems, informing decisions in the design of future automated vehicles.
ISSN:2169-5067
1071-1813
2169-5067
DOI:10.1177/1071181322661476