How does training influence use and understanding of advanced vehicle technologies: a simulator evaluation of driver behavior and mental models
DOI:
https://doi.org/10.55329/udqk4583Keywords:
ADAS, ACC, driver training, driving simulation, mental models, vehicle automationAbstract
Advanced vehicle technologies such as Advanced Driver Assistance Systems (ADAS) promise increased safety and convenience but are also sophisticated and complex. Their presence in vehicles affects how drivers interact with the technologies and how drivers must know about these technologies. To maximize safety benefits, drivers must use such systems appropriately. They must understand how these technologies work and how they may change drivers' traditional responsibilities. Training has been recognized as an effective tool for accelerating knowledge and skills in traditional driving. Consequently, training is gaining recognition as an important tool for improving drivers' knowledge, understanding, and appropriate use of vehicle technologies as well. This study evaluated the effects of different training methods on drivers' use and understanding of vehicle automation, specifically Adaptive Cruise Control (ACC). Licensed drivers with little to no experience with ADAS features were randomly assigned into groups based on three training conditions: two experimental groups, ‘User Manual’ and ‘Visualization’, and a control group with a ‘Sham’ training. Participants were surveyed on their understanding of Adaptive Cruise Control before and after training. They also drove an advanced driving simulator equipped with ACC. The simulated drive offered multiple opportunities for the drivers to interact with the ACC and included embedded cues for engaging with the system and embedded probes to measure driver awareness of the system state. The results found a significant overall increase in knowledge of ACC after training for the experimental groups. Drivers in the experimental training groups also had better real-time awareness of the system state than the control group. The results indicate that training is associated with improved knowledge about the systems. It also shows differential effects of different approaches to training, with text-based training showing greater improvement. These findings have important implications for the design and deployment of these systems, and for policies around driver licensing and education.
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Author Biographies
Anuj K. Pradhan, University of Massachusetts Amherst, USA
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, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing
Apoorva P. Hungund, University of Massachusetts Amherst, USA
Apoorva P. 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, Methodology, Software, Formal analysis, Visualization, Writing—original draft, Writing—review & editing
Ganesh Pai, University of Massachusetts Amherst, USA
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: Methodology, Software, Visualization, Writing—review & editing
Jaji Pamarthi, University of Massachusetts Amherst, USA
Jaji Pamarthi is a PhD student in Industrial Engineering at University of Massachusetts Amherst. She has a Master degree in Robotics from Rochester Institute of Technology. Her research interests lies in drivers behavior with focus on human-machine interaction, physiological signals and machine learning.
CRediT contribution: Data curation, Software, Visualization, Writing—review & editing
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Copyright (c) 2023 Anuj K. Pradhan, Apoorva P. Hungund, Ganesh Pai, Jaji Pamarthi
This work is licensed under a Creative Commons Attribution 4.0 International License.
