Does training improve users' mental models about adaptive cruise control?
DOI:
https://doi.org/10.55329/aqze5695Keywords:
adaptive cruise control (ACC), advanced driver assistance systems (ADAS), driver training, mental modelsAbstract
While Advanced Driver Assistance Systems (ADAS) promise safety benefits to drivers, there is evidence to suggest that drivers are unaware or uninformed about their vehicles’ systems and thus have poor mental models about the systems. Previous studies suggest that training improves drivers’ mental models, although some studies report limited impacts. This study investigated the relationship between training and drivers’ mental models about Adaptive Cruise Control (ACC), compared the impact of two different training approaches on drivers’ mental models, and examined the relationship between driver knowledge and trust regarding ADAS technologies. This study was conducted online, and participants were randomly and equally assigned to one of three training groups – owner’s manual (text-based); state diagram visualization; or sham (control). Surveys measured their trust and mental models about ACC before and after training. The results found that the text-based group outperformed the visualization group and the control group in terms of post-training overall mental model scores, but these differences were not statistically significant. No correlation between post-training mental model scores and overall trust scores was found. This study provides evidence that training improves users’ mental models about technology and finds that different training platforms or paradigms may affect learning differently.
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Author Biographies
Apoorva Pramod Hungund, University of Massachusetts Amherst, the United States of America
Apoorva Pramod Hungund is a PhD student in Industrial Engineering at the University of Massachusetts Amherst. Her interests lie in driving behavior of teenagers, and studying the correlation between distracted driving and use of automated vehicle systems.
CRediT contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing—original draft, Writing—review & editing.
Ganesh Pai, University of Massachusetts Amherst, the United States of America
Ganesh Pai is a PhD candidate in Industrial Engineering at the University of Massachusetts Amherst. His research domain is human factors in transportation research, especially driver behavioral research in the context of vehicle automation and development of training approaches to improve driver behaviors and performance.
CRediT contribution: Conceptualization, Investigation, Methodology, Visualization, Writing—original draft, Writing—review & editing.
Anuj K. Pradhan, University of Massachusetts Amherst, the United States of America
Anuj Pradhan is an assistant professor in Industrial Engineering at the University of Massachusetts Amherst. He has a doctoral degree from the same university (2009), and his research interests lie in driver behavior and traffic safety with a focus on the human factors of advanced vehicle technologies and automation, young and novice drivers, and training and intervention.
CRediT contribution: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing—original draft, Writing—review & editing.
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Copyright (c) 2024 Apoorva Pramod Hungund, Ganesh Pai, Anuj K. Pradhan
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