How does training influence use and understanding of advanced vehicle technologies: a simulator evaluation of driver behavior and mental models

Authors

DOI:

https://doi.org/10.55329/udqk4583

Keywords:

ADAS, ACC, driver training, driving simulation, mental models, vehicle automation

Abstract

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

References

Abraham, H., B. Reimer, B. Mehler (2017), 'Advanced Driver Assistance Systems (ADAS): A Consideration of Driver Perceptions on Training, Usage & Implementation', Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61, 1954–1958. DOI: https://doi.org/10.1177/1541931213601967

Abraham, H., B. Reimer, B. Mehler (2018), 'Learning to Use In-Vehicle Technologies: Consumer Preferences and Effects on Understanding', Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62, 1589–1593. DOI: https://doi.org/10.1177/1541931218621359

Bengler, K., K. Dietmayer, B. Farber, M. Maurer, C. Stiller, H. Winner (2014), 'Three Decades of Driver Assistance Systems: Review and Future Perspectives', IEEE Intelligent Transportation Systems Magazine, 6(4), 6–22. DOI: https://doi.org/10.1109/MITS.2014.2336271

Blömacher, K., G. Nöcker, M. Huff (2018), 'The role of system description for conditionally automated vehicles', Transportation Research Part F: Traffic Psychology and Behaviour, 54, 159–170. DOI: https://doi.org/10.1016/j.trf.2018.01.010

Boelhouwer, A., A. P. van den Beukel, M. C. van der Voort, M. H. Martens (2019), 'Should I take over? Does system knowledge help drivers in making take-over decisions while driving a partially automated car?', Transportation Research Part F: Traffic Psychology and Behaviour, 60, 669–684. DOI: https://doi.org/10.1016/j.trf.2018.11.016

Braun, H., M. Gärtner, S. Trösterer, L. E. Akkermans, M. Seinen, A. Meschtscherjakov, M. Tscheligi (2019), 'Advanced driver assistance systems for aging drivers: Insights on 65+ drivers’ acceptance of and intention to use ADAS', 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Utrecht, the Netherlands, 21–25 September 2019. DOI: https://doi.org/10.1145/3342197.3344517

Carroll, J. M., J. R. Olson (1988), ‘Mental models in human-computer interaction’, in Helander, M. (ed.), Handbook of human-computer interaction (Amsterdam, the Netherlands: North-Holland)

Feinauer, S., L. Schuller, I. Groh, L. Huestegge, T. Petzoldt (2022), 'The potential of gamification for user education in partial and conditional driving automation: A driving simulator study', Transportation Research Part F: Traffic Psychology and Behaviour, 90, 252–268. DOI: https://doi.org/10.1016/j.trf.2022.08.009

Forster, Y., S. Hergeth, F. Naujoks, J. Krems, A. Keinath (2019), 'User Education in Automated Driving: Owner’s Manual and Interactive Tutorial Support Mental Model Formation and Human-Automation Interaction', Information, 10(4), 143. DOI: https://doi.org/10.3390/info10040143

Gaspar, J. G., C. Carney, E. Shull, W. Horrey (2020), 'The Impact of Driver’s Mental Models of Advanced Vehicle Technologies on Safety and Performance', AAA Foundation for Traffic Safety (Washington DC, USA), Technical report.

Jian, J.-Y., A. M. Bisantz, C. G. Drury (2000), 'Foundations for an Empirically Determined Scale of Trust in Automated Systems', International Journal of Cognitive Ergonomics, 4(1), 53–71. DOI: https://doi.org/10.1207/S15327566IJCE0401_04

Koustanaï, A., V. Cavallo, P. Delhomme, A. Mas (2012), 'Simulator training with a forward collision warning system: Effects on driver-system interactions and driver trust', Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 709–721. DOI: https://doi.org/10.1177/0018720812441796

Krampell, M., I. Solís-Marcos, M. Hjälmdahl (2020), 'Driving automation state-of-mind: Using training to instigate rapid mental model development', Applied Ergonomics, 83, 102986. DOI: https://doi.org/10.1016/j.apergo.2019.102986

Mehlenbacher, B., M. S. Wogalter, K. R. Laughery (2002), 'On the Reading of Product Owner’s Manuals: Perceptions and Product Complexity', Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 46, 730–734. DOI: https://doi.org/10.1177/154193120204600610

Merat, N., J. D. Lee (2012), 'Preface to the Special Section on Human Factors and Automation in Vehicles: Designing Highly Automated Vehicles With the Driver in Mind', Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 681–686. DOI: https://doi.org/10.1177/0018720812461374

Pai, G., A. P. Hungund, S. Widrow, J. Radadiya, A. Pradhan (2021), 'Users’ Perception Of Training Approaches For Advanced Driver Assistance Systems (Adas)', Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65, 279–283. DOI: https://doi.org/10.1177/1071181321651266

Pradhan, A. K., G. Divekar, K. Masserang, M. Romoser, T. Zafian, R. D. Blomberg, F. D. Thomas, I. Reagan, M. Knodler, A. Pollatsek, D. L. Fisher (2011), 'The effects of focused attention training on the duration of novice drivers’ glances inside the vehicle', Ergonomics, 54(10), 917–931. DOI: https://doi.org/10.1080/00140139.2011.607245

Pradhan, A. K., G. Pai, A. P. Hungund, J. Pamarthi (2022), 'Does Training Improve Users’ Mental Models about Adaptive Cruise Control?'

Pradhan, A. K., G. Pai, J. Radadiya, M. A. Knodler, C. Fitzpatrick, W. J. Horrey (2020), 'Proposed Framework for Identifying and Predicting Operator Errors When using Advanced Vehicle Technologies', Transportation Research Record: Journal of the Transportation Research Board, 2674(10), 105–113. DOI: https://doi.org/10.1177/0361198120938778

Pradhan, A. K., J. Sullivan, C. Schwarz, F. Feng, S. Bao (2019), 'Training and education: Human factors considerations for automated driving systems', in Meyer, G., S. Beiker (eds.), Road Vehicle Automation 5. Lecture Notes in Mobility, (Cham, Switzerland: Springer). DOI: https://doi.org/10.1007/978-3-319-94896-6_7

Reimer, B., B. Mehler, J. F. Coughlin (2010), 'An evaluation of driver reactions to new vehicle parking assist technologies developed to reduce driver stress', New England University Transportation Center, Massachusetts Institute of Technology (Cambridge, MA, USA), Technical report.

Sportillo, D., A. Paljic, L. Ojeda (2018), 'Get ready for automated driving using Virtual Reality', Accident Analysis & Prevention, 118, 102–113. DOI: https://doi.org/10.1016/j.aap.2018.06.003

Sportillo, D., A. Paljic, L. Ojeda, P. Fuchs, V. Roussarie (2018), 'Light Virtual Reality Systems for the Training of Conditionally Automated Vehicle Drivers', IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Tuebingen/Reutlingen, Germany, 18–22 March 2018. DOI: https://doi.org/10.1109/VR.2018.8446226

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Published

2023-03-28

How to Cite

Pradhan, A., Hungund, A., Pai, G., & Pamarthi, J. (2023). How does training influence use and understanding of advanced vehicle technologies: a simulator evaluation of driver behavior and mental models. Traffic Safety Research, 3, 000024. https://doi.org/10.55329/udqk4583