Characterizing Driver Trust in Vehicle Control Algorithm Parameters
Human factors research in vehicle automation has focused on user interfaces such as performance feedback through visual and auditory displays (Blanco et al., 2015). Another approach is to use vehicle dynamics and vibrations as communicative tools for guiding attention (e.g., Morando, Victor, & Dozza, 2016; Walker, Stanton, & Young, 2006; Wiese & Lee, 2007). In a previous study (Price, Venkatraman, Gibson, Lee, & Mutlu, 2016), the authors showed that the steering wheel deadband, or lateral movement of the vehicle while maintaining lane position, was negatively associated with trust—more lateral movement led to less trust in the algorithm. The present study extends these findings by using Bayesian statistical methods with new control algorithm data. Although the inclusion of additional algorithm characteristics did not improve the trust model, the use of Bayesian statistical methods provides a useful tool to incorporate prior knowledge into an analysis.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/15419312
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Supplemental Notes:
- © 2018 by Human Factors and Ergonomics Society.
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Authors:
- Domeyer, Joshua
- Venkatraman, Vindhya
- Price, Morgan
- Lee, John D
- Publication Date: 2018-9
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1821-1825
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Serial:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume: 62
- Issue Number: 1
- Publisher: Sage Publications, Incorporated
- ISSN: 2169-5067
- EISSN: 1071-1813
- Serial URL: http://journals.sagepub.com/home/pro
Subject/Index Terms
- TRT Terms: Algorithms; Automated vehicle control; Bayes' theorem; Drivers; Human factors; Psychological trust
- Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01710733
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 11 2019 5:26PM