Research on Enhanced Situation Awareness Model with DMI Visualization Cues for High-Speed Train Driving

References

• Atashfeshan, N., Saidi-Mehrabad, M., & Razavi, H. (2021). A novel dynamic function allocation method in human-machine systems focusing on trigger mechanism and allocation strategy. Reliability Engineering & System Safety, 207(2021), 107337. https://doi.org/10.1016/j.ress.2020.107337
   Web of Science ®Google Scholar
• Biondi, F., Alvarez, I., & Jeong, K.-A. (2019). Human–vehicle cooperation in automated driving: A Multidisciplinary review and appraisal. International Journal of Human–Computer Interaction, 35(11), 932–946. https://doi.org/10.1080/10447318.2018.1561792
   Web of Science ®Google Scholar
• Brandenburger, N., Hörmann, H.-J., Stelling, D., & Naumann, A. (2017). Tasks, skills, and competencies of future high-speed train drivers. Proceedings of the Institution of Mechanical Engineers, Part F, 231(10), 1115–1122. https://doi.org/10.1177/0954409716676509
   Google Scholar
• Bruder, C., & Hasse, C. (2019). Differences between experts and novices in the monitoring of automated systems. International Journal of Industrial Ergonomics, 72(2019), 1–11. https://doi.org/10.1016/j.ergon.2019.03.007
   Web of Science ®Google Scholar
• Bruder, C., & Hasse, C. (2020). What the eyes reveal: Investigating the detection of automation failures. Applied Ergonomics, 82(2020), 102967. https://doi.org/10.1016/j.apergo.2019.102967
   PubMed Web of Science ®Google Scholar
• Cabon, P., Coblentz, A., Mollard, R., & Fouillot, J. P. (1993). Human vigilance in railway and long-haul flight operation. Ergonomics, 36(9), 1019–1033. https://doi.org/10.1080/00140139308967974
   PubMed Web of Science ®Google Scholar
• Dunn, N., & Williamson, A. (2012). Driving monotonous routes in a train simulator: The effect of task demand on driving performance and subjective experience. Ergonomics, 55(9), 997–1008. https://doi.org/10.1080/00140139.2012.691994
   PubMed Web of Science ®Google Scholar
• Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64. https://doi.org/10.1518/001872095779049543
   Web of Science ®Google Scholar
• Endsley, M. R., & Kaber, D. B. (1999). Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics, 42(3), 462–492. https://doi.org/10.1080/001401399185595
   PubMed Web of Science ®Google Scholar
• Endsley, M. R. (2021). A systematic review and meta-analysis of direct objective measures of situation awareness: A comparison of SAGAT and SPAM. Human Factors, 63(1), 124–150. https://doi.org/10.1177/0018720819875376
   PubMed Web of Science ®Google Scholar
• Grundgeiger, T., Hohm, A., Michalek, A., Egenolf, T., Markus, C., & Happel, O. (2022). The validity of the SEEV model as a process measure of situation awareness: The example of a simulated endotracheal intubation. Human Factors, 64(7), 1181–1194. https://doi.org/10.1177/0018720821991651
   PubMed Web of Science ®Google Scholar
• Guo, B., Mao, Y., Hedge, A., & Fang, W. (2015). Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator. International Journal of Industrial Ergonomics, 48(2015), 99–109. https://doi.org/10.1016/j.ergon.2015.04.005
   Web of Science ®Google Scholar
• Guo, M., Hu, L., & Ye, L. (2019). Cognition and driving safety: How does the high-speed railway drivers’ cognitive ability affect safety performance? Transportation Research Part F: Traffic Psychology and Behaviour, 65(2019), 10–22. https://doi.org/10.1016/j.trf.2019.07.006
   Web of Science ®Google Scholar
• Hamilton, W. I., & Clarke, T. (2005). Driver performance modelling and its practical application to railway safety. Applied Ergonomics, 36(6), 661–670. https://doi.org/10.1016/j.apergo.2005.07.005
   PubMed Web of Science ®Google Scholar
• Hansen, C., Daim, T., Ernst, H., & Herstatt, C. (2016). The future of rail automation: A scenario-based technology roadmap for the rail automation market. Technological Forecasting and Social Change, 110(2016), 196–212. https://doi.org/10.1016/j.techfore.2015.12.017
   Web of Science ®Google Scholar
• Harrison, C., Stow, J., Ge, X., Gregory, J., Gibson, H., & Monk, A. (2022). At the limit? Using operational data to estimate train driver human reliability. Applied Ergonomics, 104(2022), 103795. https://doi.org/10.1016/j.apergo.2022.103795
   PubMed Web of Science ®Google Scholar
• Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in psychology (Vol. 52, pp. 139–183). Elsevier.
   Google Scholar
• Hickey, A. R., & Collins, M. D. (2017). Disinhibition and train driver performance. Safety Science, 95(2017), 104–115. https://doi.org/10.1016/j.ssci.2017.02.016
   Web of Science ®Google Scholar
• Hou, M., Zhu, H., Zhou, M., & Arrabito, G. R. (2011). Optimizing operato-agent interaction in intelligent adaptive interface design: A conceptual framework. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 41(2), 161–178. https://doi.org/10.1109/TSMCC.2010.2052041
   Web of Science ®Google Scholar
• Jiang, J., Karran, A. J., Coursaris, C. K., Léger, P.-M., & Beringer, J. (2023). A situation awareness perspective on human-ai interaction: Tensions and opportunities. International Journal of Human–Computer Interaction, 39(9), 1789–1806. https://doi.org/10.1080/10447318.2022.2093863
   Google Scholar
• Kircher, K., & Ahlström, C. (2020). Attentional requirements on cyclists and drivers in urban intersections. Transportation Research Part F: Traffic Psychology and Behaviour, 68(2020), 105–117. https://doi.org/10.1016/j.trf.2019.12.008
   Web of Science ®Google Scholar
• Klein, G. A., Calderwood, R., & MacGregor, D. (1989). Critical decision method for eliciting knowledge. IEEE Transactions on Systems, Man, and Cybernetics, 19(3), 462–472. https://doi.org/10.1109/21.31053
   Web of Science ®Google Scholar
• Knisely, B. M., Joyner, J. S., Rutkowski, A. M., Wong, M., Barksdale, S., Hotham, H., Kharod, K., & Vaughn-Cooke, M. (2020). A cognitive decomposition to empirically study human performance in control room environments. International Journal of Human-Computer Studies, 141(2020), 102438. https://doi.org/10.1016/j.ijhcs.2020.102438
   Web of Science ®Google Scholar
• Li, S., Cai, B., Liu, J., & Wang, J. (2018). Collision risk analysis based train collision early warning strategy. Accident Analysis & Prevention, 112(2018), 94–104. https://doi.org/10.1016/j.aap.2017.11.039
   PubMed Web of Science ®Google Scholar
• Li, W.-C., Horn, A., Sun, Z., Zhang, J., & Braithwaite, G. (2020). Augmented visualization cues on primary flight display facilitating pilot’s monitoring performance. International Journal of Human-Computer Studies, 135(2020), 102377. https://doi.org/10.1016/j.ijhcs.2019.102377
   Web of Science ®Google Scholar
• Lin, C. J., Yenn, T.-C., & Yang, C.-W. (2010). Optimizing human–system interface automation design based on a skill-rule-knowledge framework. Nuclear Engineering and Design, 240(7), 1897–1905. https://doi.org/10.1016/j.nucengdes.2010.03.026
   Web of Science ®Google Scholar
• Lo, J. C., Sehic, E., Brookhuis, K. A., & Meijer, S. A. (2016). Explicit or implicit situation awareness? Measuring the situation awareness of train traffic controllers. Transportation Research Part F: Traffic Psychology and Behaviour, 43(2016), 325–338. https://doi.org/10.1016/j.trf.2016.09.006
   Web of Science ®Google Scholar
• McLeod, R. W., Walker, G. H., & Moray, N. (2005). Analysing and modelling train driver performance. Applied Ergonomics, 36(6), 671–680. https://doi.org/10.1016/j.apergo.2005.05.006
   PubMed Web of Science ®Google Scholar
• Naweed, A. (2014). Investigations into the skills of modern and traditional train driving. Applied Ergonomics, 45(3), 462–470. https://doi.org/10.1016/j.apergo.2013.06.006
   PubMed Web of Science ®Google Scholar
• Naweed, A., Balakrishnan, G., & Dorrian, J. (2018). Going solo: Hierarchical task analysis of the second driver in “two-up” (multi-person) freight rail operations. Applied Ergonomics, 70(2018), 202–231. https://doi.org/10.1016/j.apergo.2018.01.002
   PubMed Web of Science ®Google Scholar
• Neisser, U. (1976). Cognition and reality: Principles and implications of cognitive psychology. Freeman.
   Google Scholar
• Pan, X., Sun, L., Ding, S., & Zuo, D. (2023). Impact analysis of situation awareness based on a multitasking difficulty quantitative model. International Journal of Human–Computer Interaction, (2023), 1–14. https://doi.org/10.1080/10447318.2023.2221601
   Google Scholar
• Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354
   PubMed Web of Science ®Google Scholar
• Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2008). Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making, 2(2), 140–160. https://doi.org/10.1518/155534308X284417
   Google Scholar
• Plant, K. L., & Stanton, N. A. (2014). All for one and one for all: Representing teams as a collection of individuals and an individual collective using a network perceptual cycle approach. International Journal of Industrial Ergonomics, 44(5), 777–792. https://doi.org/10.1016/j.ergon.2014.05.005
   Web of Science ®Google Scholar
• Rao, X., Montigel, M., & Weidmann, U. (2016). A new rail optimisation model by integration of traffic management and train automation. Transportation Research Part C: Emerging Technologies, 71(2016), 382–405. https://doi.org/10.1016/j.trc.2016.08.011
   Web of Science ®Google Scholar
• Rebensky, S., Carroll, M., Bennett, W., & Hu, X. (2022). Impact of heads-up displays on small unmanned aircraft system operator situation awareness and performance: A simulated study. International Journal of Human–Computer Interaction, 38(5), 419–431. https://doi.org/10.1080/10447318.2021.1948683
   Web of Science ®Google Scholar
• Revell, K. M. A., Richardson, J., Langdon, P., Bradley, M., Politis, I., Thompson, S., Skrypchuck, L., O'Donoghue, J., Mouzakitis, A., & Stanton, N. A. (2020). Breaking the cycle of frustration: Applying Neisser’s Perceptual Cycle Model to drivers of semi-autonomous vehicles. Applied Ergonomics, 85(2020), 103037. https://doi.org/10.1016/j.apergo.2019.103037
   PubMed Web of Science ®Google Scholar
• Roth, E., & Multer, J. (2009). Technology implications of a cognitive task analysis for locomotive engineers (DOT/FRA/ORD-09/03). Department of Transportation, Federal Railroad Administration. http://www.fra.dot.gov/downloads/research/ord0903.pdf.
   Google Scholar
• Röttger, S., Bali, K., & Manzey, D. (2009). Impact of automated decision aids on performance, operator behaviour and workload in a simulated supervisory control task. Ergonomics, 52(5), 512–523. https://doi.org/10.1080/00140130802379129
   PubMed Web of Science ®Google Scholar
• Salas, E., & Dietz, A. S. (2017). Situational awareness (E. Salas, Ed., 1st ed.). Routledge.
   Google Scholar
• Salmon, P., Stanton, N., Walker, G., & Green, D. (2006). Situation awareness measurement: A review of applicability for C4i environments. Applied Ergonomics, 37(2), 225–238. https://doi.org/10.1016/j.apergo.2005.02.001
   PubMed Web of Science ®Google Scholar
• Seppelt, B. D., & Lee, J. D. (2019). Keeping the driver in the loop: Dynamic feedback to support appropriate use of imperfect vehicle control automation. International Journal of Human-Computer Studies, 125(2019), 66–80. https://doi.org/10.1016/j.ijhcs.2018.12.009
   Web of Science ®Google Scholar
• Stanton, N. A., Stewart, R., Harris, D., Houghton, R. J., Baber, C., McMaster, R., Salmon, P., Hoyle, G., Walker, G., Young, M. S., Linsell, M., Dymott, R., & Green, D. (2006). Distributed situation awareness in dynamic systems: Theoretical development and application of an ergonomics methodology. Ergonomics, 49(12–13), 1288–1311. https://doi.org/10.1080/00140130600612762
   PubMed Web of Science ®Google Scholar
• Steelman, K. S., McCarley, J. S., & Wickens, C. D. (2017). Theory-based models of attention in visual workspaces. International Journal of Human–Computer Interaction, 33(1), 35–43. https://doi.org/10.1080/10447318.2016.1232228
   Web of Science ®Google Scholar
• The JAMOVI Project. (2023). JAMOVI (Version 2.3.18.0) [Computer Software]. https://www.jamovi.org (accessed on 10 August 2023).
   Google Scholar
• van der Weide, R., van der Vlies, V., & van der Meer, F. (2022). Train driver experience: A big data analysis of learning and retaining the new ERTMS system. Applied Ergonomics, 99(2022), 103627. https://doi.org/10.1016/j.apergo.2021.103627
   PubMed Web of Science ®Google Scholar
• Vanderhaegen, F., Burkhardt, J.-M., Fang, W., Naumann, A., & Havârneanu, G. M. (2021). Human factors and automation in future railway systems. Cognition, Technology & Work, 23(2), 189–192. https://doi.org/10.1007/s10111-021-00670-3
   Web of Science ®Google Scholar
• Wang, A., Guo, B., Du, H., & Bao, H. (2022). Impact of automation at different cognitive stages on high-speed train driving performance. IEEE Transactions on Intelligent Transportation Systems, 23(12), 24599–24608. https://doi.org/10.1109/TITS.2022.3211709
   Web of Science ®Google Scholar
• Wei, S., Yan, X.-H., Cai, B.-G., & Wang, J. (2015). Multiobjective optimization for train speed trajectory in CTCS high-speed railway with hybrid evolutionary algorithm. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2215–2225. https://doi.org/10.1109/TITS.2015.2402160
   Google Scholar
• Weiss, H., Liu, A., Byon, A., Blossom, J., & Stirling, L. (2021). Comparison of display modality and human-in-the-loop presence for on-orbit inspection of spacecraft. Human Factors: The Journal of the Human Factors and Ergonomics Society, (2021), 1–15. https://doi.org/10.1177/00187208211042782
   Google Scholar
• Wickens, C. D. (2008). Situation awareness: Review of Mica Endsley’s 1995 articles on situation awareness theory and measurement. Human Factors, 50(3), 397–403. https://doi.org/10.1518/001872008X288420
   PubMed Web of Science ®Google Scholar
• Wickens, C. D., Goh, J., Helleberg, J., Horrey, W. J., & Talleur, D. A. (2003). Attentional models of multitask pilot performance using advanced display technology. Human Factors, 45(3), 360–380. https://doi.org/10.1518/hfes.45.3.360.27250
   PubMed Web of Science ®Google Scholar
• Yan, R., Wu, C., & Wang, Y. (2018). Exploration and evaluation of individual difference to driving fatigue for high-speed railway: A parametric SVM model based on multidimensional visual cue. IET Intelligent Transport Systems, 12(6), 504–512. https://doi.org/10.1049/iet-its.2017.0289
   Web of Science ®Google Scholar
• Yang, B., Saito, T., Wang, Z., Kitazaki, S., & Nakano, K. (2022). Influences of different traffic information on driver behaviors while interacting with oncoming traffic in level 2 automated driving. International Journal of Human–Computer Interaction, (2020), 1–9. https://doi.org/10.1080/10447318.2022.2121202
   Google Scholar
• Zhang, T., Yang, J., Liang, N., Pitts, B. J., Prakah-Asante, K. O., Curry, R., Duerstock, B. S., Wachs, J. P., & Yu, D. (2020). Physiological measurements of situation awareness: A systematic review. Human Factors: The Journal of the Human Factors and Ergonomics Society, 65(5), 737–758. https://doi.org/10.1177/0018720820969071
   Google Scholar
• Zhang, Y., Wang, H., Chai, M., & Cheng, R. (2021). Novel graph-based train control data verification method for Chinese train control system. IEEE Intelligent Transportation Systems Magazine, 13(3), 45–57. https://doi.org/10.1109/MITS.2019.2953506
   Web of Science ®Google Scholar
• Zhang, Y., Wang, H., Yuan, T., Lv, J., & Xu, T. (2019). Hybrid online safety observer for CTCS-3 train control system on-board equipment. IEEE Transactions on Intelligent Transportation Systems, 20(3), 925–934. https://doi.org/10.1109/TITS.2018.2836459
   Web of Science ®Google Scholar