Artificial Neural Networks as a tool for ergonomic evaluations of vehicle control panels

• Autorzy: Hałacz Joanna , Neugebauer Maciej

Identyfikatory

DOI

10.31648/ts.8588

• Języki publikacji: EN

Abstrakty

EN

Unreadable and inconveniently arranged instruments make it difficult for the driver to accurately read signals and understand the relayed information. They can distract the driver and prolong response times, thus posing a risk to traffic safety. Designers also have to account for customer expectations, including a demand for esthetically appealing dashboards that incorporate vast amounts of data in limited space since such dashboards appear to be maximally adapted to the driver’s needs. However, attractive dashboards are not always adapted to human perceptual abilities. A neural model was developed in the study to objectively assess dashboard ergonomics in passenger cars. The data were used to determine the correlations between subjective driver impressions and the functionality and ergonomics of dashboards evaluated objectively based on the adopted criteria. With the best-learned networks, 3 conformance classes were obtained for the predicted cases. However, taking into account the ± 1 class, as many as 3 of the preserved ANN gave correct answers in all 6 cases.

Słowa kluczowe

EN

dashboard design; vehicle ergonomics; human-machine interaction; artificial neural networks; ergonomics of signaling devices; driver behavior

• Wydawca: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego w Olsztynie
• Czasopismo: Technical Sciences / University of Warmia and Mazury in Olsztyn
• Rocznik: 2023
• Tom: nr 26(1)
• Strony: 77--96
• Opis fizyczny: Bibliogr. 50 poz., tab., wykr.

Twórcy

autor

Hałacz Joanna

• Faculty of Technical Sciences University of Warmia and Mazury in Olsztyn

• https://orcid.org/0000-0002-2711-3887

autor

Neugebauer Maciej

• Faculty of Technical Sciences University of Warmia and Mazury in Olsztyn

• https://orcid.org/0000-0002-9067-3609

Bibliografia

• Almonacid F., Rus C., Pérez-Higueras P., Hontoria L. 2011. Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks. Energy, 36: 375-384. https://doi.org/10.1016/j.energy.2010.10.028.
• Baldwin C.L. 2002. Designing in-vehicle technologies for older drivers: Application of sensorycognitive interaction theory. Theoretical Issues In Ergonomic Science, 3(4): 307-329. https://doi.org/10.1080/1463922021000009029.
• Bastien J.M.C., Scapin D.L. 1992. A validation of ergonomic criteria for the evaluation of human‐ computer interfaces. International Journal of Human-Computer Interaction, 4(2): 183-196. https://www.tandfonline.com/doi/abs/10.1080/10447319209526035.
• Bhattacharya S., Bisht D.S. 2021. Usability Analysis ofWarning Icons inPassengerCarDashboards in India Using Modified System Usability Scale (SUS). In: Design for Tomorrow. Volume 1. Eds. A. Chakrabarti, R. Poovaiah, P. Bokil, V. Kant. ICoRD 2021. Smart Innovation, Systems and Technologies, 221. Springer, Singapore. https://doi.org/10.1007/978-981-16-0041-8_30.
• Burnett G.E., Porter J.M. 2001. Ubiquitous computing within cars: designing controls for nonvisual use. International Journal of Human-Computer Studies, 55(4 ): 521-531. https://doi.org/10.1006/ijhc.2001.0482.
• Carvalho R., Soares M. 2012. Ergonomic and usability analysis on a sample of automobile dashboards. Work, 41(Supplement 1): 1507-1514. https://doi.org/10.3233/WOR-2012-0345-1507.
• François M., Osiurak F., Fort A., Crave P., Navarro J. 2021. Usability and acceptance of truck dashboards designed by drivers: Two participatory design approaches compared to a usercentered design. International Journal of Industrial Ergonomics, 81: 103073, https://doi.org/10.1016/j.ergon.2020.103073.
• Ghasemi F., Kalatpour O., Moghimbeigi A., Mohammadfam I. 2017. A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces. Iranian Journal of Health, Safety and Environment, 4(4): 835-843.
• Gibson M., Lee J., Venkatraman V., Price M., Lewis J., Montgomery O., Foley J. 2016. Situation Awareness, Scenarios, and Secondary Tasks: Measuring Driver Performance and Safety Margins in Highly Automated Vehicles. SAE International Journal of Passenger Cars-Electronic and Electrical Systems, 9: 237-242. https://doi.org/10.4271/2016-01-0145.
• Gibson Z., Butterfield J., Marzano A. 2016. User-centered design criteria in next generation vehicle consoles. Procedia CIRP, 55(2016): 260-265. https://doi.org/10.1016/j.procir.2016.07.024.
• Gkouskos D., Normark C.J., Lundgren S. 2014. What drivers really want: Investigating dimensions in automobile user needs. International Journal of Design, 8(1): 59-71.
• Haj Mahmoud O., Pontonnier C., Dumont G., Poli S., Multon F. 2021. A neural networks approach to determine factors associated with self-reported discomfort in picking tasks. Human Factors. https://doi.org/10.1177/00187208211047640.
• Kim S.J,. Dey A.K., Lee J., Forlizzi J. 2011. Usability of car dashboard displays for elder drivers. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, p. 493-502.
• Klauer S.G., Guo F., Simons-Morton B.G., Ouimet M.C., Lee S.E., Dingus T.A. 2014. Distracted driving and risk of road crashes among novice and experienced drivers. New England Journal of Medicine, 370(1): 54-59. https://doi.org/10.1056/NEJMsa1204142.
• Kolich M., Seal N., Taboun S. 2004. Automobile seat comfort prediction: statistical model vs. artificial neural network. Applied Ergonomics, 35(3): 275-284. https://doi.org/10.1016/j.apergo.2004.01.007.
• Królczyk J., Matuszek D., Tukiendorf M. 2008. Wykorzystanie sieci neuronowych (fbm) do modelowania procesu mieszania dwuskładnikowych układów ziarnistych. Inżynieria Rolnicza, 7(105): 117-122.
• Kumar A., Mohan D., Patel R., Varghese M. 2002. Development of grain threshers based on ergonomic design criteria. Applied Ergonomics, 33(5): 503-508. https://doi.org/10.1016/ S0003-6870(02)00029-7.
• Kwater T., Kędzior Z., Twaróg B. 2001. Estimation by artificial neural network in ecological problems. AMSE-Conference, Lviv (Ukraine), p. 212-215.
• Landau K.E. 2002. The development of driver assistance systems following usability criteria. Behaviour and Information Technology, 21(5): 341-344. https://doi.org/10.1080/0144929021000048457.
• Lin C.J., Belis T.T., Kuo T.C. 2019. Ergonomics-Based Factors or Criteria for the Evaluation of Sustainable Product Manufacturing. Sustainability, 11: 4955. https://doi.org/10.3390/su11184955.
• Liu J., Yu S., Chu J. 2020. Comfort Evaluation of an Aircraft Cabin System Employing a Hybrid Model. Sustainability, 12: 8503. https://doi.org/10.3390/su12208503.
• Ma J., Zuo Y., Gong Z. 2019. Prediction Model of Driving Performance Indicators in Center Control Screen Location Layout Based On BP Neural Network. 2nd International Conference on Information Science and Electronic Technology (ISET 2019). https://doi.org/10.23977/iset.2019.053.
• Marcus A. 2015. The Next Revolution: Vehicle User Interfaces. HCI and User-Experience Design. Springer, London, p. 91-100.
• Meiring G.A.M., Myburgh H.C. 2015. A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors, 15(12): 30653-30682. https://doi.org/10.3390/s151229822.
• Menanno M., Savino M.M., Ciarapica F.E. 2021. Exploring continuous improvement for safety management systems through artificial neural networks. International Journal of Product Development, 25(3): 213-241.
• Naddeo A., Cappetti N., Ippolito O. 2014. Dashboard Reachability and Usability Tests: A Cheap and Effective Method for Drivers’ Comfort Rating. SAE International in United States, Technical Paper 2014-01-0455. https://doi.org/10.4271/2014-01-0455.
• Nandy A., Goucher-Lambert K. 2022. Do Human and Computational Evaluations of Similarity Align? An Empirical Study of Product Function. ASME. Journal of Mechanical Design, 144(4): 041404. https://doi.org/10.1115/1.4053858.
• Ou Y.K., Liu Y.C., Shih F.Y. 2013. Risk prediction model for drivers’ in-vehicle activities - Application of task analysis and back-propagation neural network. Transportation Research. Part F. Traffic Psychology and Behaviour, 18, 83-93. https://doi.org/10.1016/j.trpro.2017.05.151.
• Park D., Park S., Kim W., Rhiu I., Myung Hwan Yun M.H. 2019. A comparative study on subjective feeling of engine acceleration sound by automobile types. International Journal of Industrial Ergonomics, 74: 102843, https://doi.org/10.1016/j.ergon.2019.102843.
• Patterson D.W. 1995. Artificial Neural Networks: theory and applications. Prentice Hall, Singapore.
• Rahimdel M., Mirzaei M., Sattarvand J., Ghodrati B., Mirzaei Nasirabad H. 2017. Artificial neural network to predict the health risk caused by whole body vibration of mining trucks. Journal of Theoretical and Applied Vibration and Acoustics, 3(1): 1-14. https://doi.org/10.22064/TAVA.2016.43016.1047.
• Rahman M.H., Schimpf C., Xie C., Sha Z. 2019. A Computer-Aided Design Based Research Platform for Design Thinking Studies. ASME. Journal of Mechanical Design, 141(12): 121102. https://doi.org/10.1115/1.4044395.
• Rutkowska D., Piliński M., Rutkowski L. 1997. Sieci neuronowe, algorytmy genetyczne i systemy rozmyte. PWN, Warszawa.
• Rutkowski L. 1996. Sieci neuronowe i neurokomputery. Wydawnictwo Politechniki Częstochowskiej, Częstochowa.
• Salah R.A., Alnahhal M.J. 2012. Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design. Applied Ergonomics, 43(6): 979-984. https://doi.org/10.1016/j.apergo.2012.01.007.
• Sha Z., Kannan K.N., Panchal J.H. 2015. Behavioral Experimentation and Game Theory in Engineering Systems Design. ASME. Journal of Mechanical Design, 137(5): 051405. https://doi.org/10.1115/1.4029767.
• Simmonds G.R.W. 1983. Ergonomics standards and research for cars Author links open overlay panel. Applied Ergonomics, 14(2): 97-101.
• Swingler K., Smith L.S. 1996. Cite as Producing a neural network for monitoring driver alertness from steering actions. Neural Computing & Applications, 4(2): 96-104.
• Tang P., Xu Sun X., Cao S. 2020. Investigating user activities and the corresponding requirements for information and functions in autonomous vehicles of the future. International Journal of Industrial Ergonomics, 80: 103044. https://doi.org/10.1016/j.ergon.2020.103044.
• Tjolleng A., Jung K., Hong W., Lee W., Lee B., You H., Son J., Park S. 2017. Classification of a Driver’s cognitive workload levels using artificial neural network on ECG signals. Applied Ergonomics, 59(A): 326-332. https://doi.org/10.1016/j.apergo.2016.09.013.
• Tovares N., Boatwright P., Cagan J. 2014. Experiential Conjoint Analysis: An ExperienceBased Method for Eliciting, Capturing, and Modeling Consumer Preference. ASME. Journal of Mechanical Design, 136(10): 101404. https://doi.org/10.1115/1.4027985.
• Tseng I., Cagan J., Kotovsky K. 2011. Learning Stylistic Desires and Generating Preferred Designs of Consumers Using Neural Networks and Genetic Algorithms. Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 5. 37th Design Automation Conference. Parts A and B. Washington, DC. August 28-31, p. 601-607. https://doi.org/10.1115/DETC2011-48642.
• Wang S., Zhang Y., Genlin G., Yang J., Wu J., Wei L. 2015. Fruit Classification by WaveletEntropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization. Entropy, 17(8): 5711-5728.
• Wellings T., Williams M., Tennant C. 2010. Understanding customers’ holistic perception of switches in automotive human – machine interfaces. Applied Ergonomics, 41(1), 8-17. https://doi.org/10.1016/j.apergo.2009.03.004.
• Wulff I.A., Westgaard R.H., Rasmussen B. 1999. Ergonomic criteria in large-scale engineering design. II. Evaluating and applying requirements in the real world of design. Applied Ergonomics, 30(3): 207-221. https://doi.org/10.1016/S0003-6870(98)00030-1.
• Yadav A.K., Chandel S.S. 2014. Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and Sustainable Energy Reviews, 33: 772-781. https://doi.org/10.1016/j.egypro.2015.07.764.
• Yang J., Coughlin J.F. 2014. In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers. International Journal of Automotive Technology, 15: 333-340. https://doi.org/10.1007/s12239-014-0034-6.
• Yegnanarayana B. 2009. Artificial Neural Networks. PHI Learning Pvt. Ltd., New Delhi.
• Yu B.Y., Honda T., Sharqawy M., Yang M. 2016. Human behavior and domain knowledge in parameter design of complex systems. Design Studies, 45, Part B: 242-267. https://doi.org/10.1016/j.destud.2016.04.005.
• Zhang Z. 2018. Artificial Neural Network. In: Multivariate Time Series Analysis in Climate and Environmental Research. Springer, Cham. https://doi.org/10.1007/978-3-319-67340-0.