A proper calibration of trust in automation is imperative to achieve optimal overall performance in human-machine systems. Previous research has suggested that human operator trust could be influenced by various situational and dispositional factors, as well as operator self-confidence. It is critical to examine what traits and factors will influence how likely a person is to trust autonomous vehicles as they become more prevalent on today's roadways. The goal of this study was to further examine the relationship between individuals' level of self-confidence in their own driving abilities and their reported trust in automation when driving semi-autonomous cars. It was hypothesized that self-confidence and level of automation would be significant predictors of participants' trust. A total of 314 participants read through a series of vignettes describing several driving scenarios and completed an online assessment that measured both their trust and self-confidence in relation to autonomous driving functions. A series of multiple regression analyses showed that driving self-confidence was a significant predictor of operator trust when using level 1 automation. Results also indicated that gender was found to be a significant predictor across all levels of automation. These results suggest that self-confidence could be good a predictor of how individuals will respond to an automated system, which may have the potential to be generalized for implementation in training and selection environments. A series of repeated measures ANOVAs were conducted to determine the effect level of automation had on trust responses. Results indicated that trust levels significantly decreased as the automation levels increased. Theoretical and practical implications are discussed. These results can inform future research that aims to determine what makes an individual more likely to accept new technologies and help those creating autonomous vehicles design features and functionality that is more likely to be trusted and effectively utilized in on-road environments.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:honorstheses-2013 |
| Date | 01 January 2021 |
| Creators | Miele, Daniela R |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Honors Undergraduate Theses |
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