Effects of Road Type and IVIS Task Type on Driver Behavior and Driving Performance in Agricultural Tractors

References

• Af Wåhlberg, A. E. (2012). Changes in driver celeration behavior over time: Do drivers learn from collisions? Transportation Research Part F: Traffic Psychology and Behaviour, 15(5), 471–479. https://doi.org/10.1016/10.1016/j.trf.2012.04.002
   Web of Science ®Google Scholar
• Ahlstrom, C., & Kircher, K. (2017). Changes in glance behavior when using a visual eco-driving system–A field study. Applied Ergonomics, 58, 414–423. https://doi.org/10.1016/j.apergo.2016.08.001
   PubMed Web of Science ®Google Scholar
• Ahmadi, A., & Machiani, S. G. (2019). Drivers’ performance examination using a personalized adaptive curve speed warning: Driving simulator study. International Journal of Human–Computer Interaction, 35(11), 996–1007. https://doi.org/10.1080/10447318.2018.1561785
   Web of Science ®Google Scholar
• Angelini, L., Baumgartner, J., Carrino, F., Carrino, S., Caon, M., Khaled, O. A., Sauer, J., Lalanne, D., Mugellini, E., & Sonderegger, A. (2016). Comparing gesture, speech and touch interaction modalities for in-vehicle infotainment systems. In IHM ‘16: Actes de la 28ième Conférence Francophone Sur L’Interaction Homme-Machine (pp. 188–196). ACM. https://doi.org/10.1145/3004107.3004118
   Google Scholar
• Anttila, V., & Luoma, J. (2005). Surrogate in-vehicle information systems and driver behavior in an urban environment: A field study on the effects of visual and cognitive load. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 121–133. https://doi.org/10.1016/j.trf.2005.04.007
   Web of Science ®Google Scholar
• Awan, U., Gölgeci, I., Makhmadshoev, D., & Mishra, N. (2022). Industry 4.0 and circular economy in an era of global value chains: What have we learned and what is still to be explored? Journal of Cleaner Production, 371, 133621. https://doi.org/10.1016/j.jclepro.2022.133621
   Web of Science ®Google Scholar
• Aydin, M. M., Gunay, B., & Akgol, K. (2019). Performance comparison of various chicane types: A driving simulator study. International Journal of Civil Engineering, 17(11), 1753–1765. https://doi.org/10.1007/s40999-019-00435-0
   Google Scholar
• Babić, D., Babić, D., Cajner, H., Sruk, A., & Fiolić, M. (2020). Effect of road markings and traffic signs presence on young driver stress level, eye movement and behavior in night-time conditions: A driving simulator study. Safety, 6(2), 24. https://doi.org/10.3390/safety6020024
   Web of Science ®Google Scholar
• Bella, F., & Silvestri, M. (2017). Interaction driver–bicyclist on rural roads: Effects of cross-sections and road geometric elements. Accident Analysis and Prevention, 102, 191–201. https://doi.org/10.1016/j.aap.2017.03.008
   PubMed Web of Science ®Google Scholar
• Birrell, S. A., & Young, M. S. (2011). The impact of smart driving aids on driving performance and driver distraction. Transportation Research Part F: Traffic Psychology and Behaviour, 14(6), 484–493. https://doi.org/10.1016/j.trf.2011.08.004
   Web of Science ®Google Scholar
• Blanco, M., Biever, W. J., Gallagher, J. P., & Dingus, T. A. (2006). The impact of secondary task cognitive processing demand on driving performance. Accident Analysis and Prevention, 38(5), 895–906. https://doi.org/10.1016/j.aap.2006.02.015
   PubMed Web of Science ®Google Scholar
• Boulagouas, W., García-Herrero, S., Chaib, R., Febres, J. D., Mariscal, M. Á., & Djebabra, M. (2020). An investigation into unsafe behaviors and traffic accidents involving unlicensed drivers: A perspective for alignment measurement. International Journal of Environmental Research and Public Health, 17(18), 6743. https://doi.org/10.3390/ijerph17186743
   PubMed Web of Science ®Google Scholar
• Brinkley, J., Posadas, B., Sherman, I., Daily, S. B., & Gilbert, J. E. (2019). An open road evaluation of a self-driving vehicle human–machine interface designed for visually impaired users. International Journal of Human–Computer Interaction, 35(11), 1018–1032. https://doi.org/10.1080/10447318.2018.1561787
   Web of Science ®Google Scholar
• Brown, T. L., Milavetz, G., Gaffney, G., & Spurgin, A. (2018). Evaluating drugged driving: Effects of exemplar pain and anxiety medications. Traffic Injury Prevention, 19(sup1), S97–S103. https://doi.org/10.1080/15389588.2017.1378814
   PubMed Web of Science ®Google Scholar
• Calvi, A. (2015). A study on driving performance along horizontal curves of rural roads. Journal of Transportation Safety & Security, 7(3), 243–267. https://doi.org/10.1080/19439962.2014.952468
   Web of Science ®Google Scholar
• Charlton, S. G., & Starkey, N. J. (2017). Driving on urban roads: How we come to expect the ‘correct’ speed. Accident Analysis & Prevention, 108, 251–260. https://doi.org/10.1016/j.aap.2017.09.010
   PubMed Web of Science ®Google Scholar
• Charlton, S. G., Starkey, N. J., Perrone, J. A., & Isler, R. B. (2014). What’s the risk? A comparison of actual and perceived driving risk. Transportation Research Part F: Traffic Psychology and Behaviour, 25, 50–64. https://doi.org/10.1016/j.trf.2014.05.003
   Web of Science ®Google Scholar
• Choudhari, T., & Maji, A. (2022). Modeling driver’s braking and steering behavior along horizontal curves of two-lane rural highways for ADAS applications. Traffic Injury Prevention, 23(7), 404–409. https://doi.org/10.1080/15389588.2022.2097669
   PubMed Web of Science ®Google Scholar
• Chu, W., Wu, C., Atombo, C., Zhang, H., & Özkan, T. (2019). Traffic climate, driver behavior, and accidents involvement in China. Accident Analysis & Prevention, 122, 119–126. https://doi.org/10.1016/j.aap.2018.09.007
   PubMed Web of Science ®Google Scholar
• Compiled by National Bureau of Statistics of China. (2020). China statistical yearbook in 2020. https://data.stats.gov.cn/easyquery.htm?cn=C01
   Google Scholar
• Cox, D. J., Owens, J. M., Barnes, L., Moncrief, M., Boukhechba, M., Buckman, S., Banton, T., & Wotring, B. (2020). A pilot study comparing newly licensed drivers with and without autism and experienced drivers in simulated and on-road driving. Journal of Autism and Developmental Disorders, 50(4), 1258–1268. https://doi.org/10.1007/s10803-019-04341-1
   PubMed Web of Science ®Google Scholar
• Deng, Z., Chu, D., Wu, C., He, Y., & Cui, J. (2019). Curve safe speed model considering driving style based on driver behavior questionnaire. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 536–547. https://doi.org/10.1016/j.trf.2018.02.007
   Web of Science ®Google Scholar
• Dingus, T. A., Guo, F., Lee, S., Antin, J. F., Perez, M., Buchanan-King, M., & Hankey, J. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences, 113(10), 2636–2641. https://doi.org/10.1073/pnas.1513271113
   PubMed Web of Science ®Google Scholar
• Dotse, J. E., & Rowe, R. (2021). Modelling Ghanaian road crash risk using the Manchester driver behavior Questionnaire. Safety Science, 139, 105213. https://doi.org/10.1016/j.ssci.2021.105213
   Web of Science ®Google Scholar
• Dukic, T., & Broberg, T. (2012). Older drivers’ visual search behavior at intersections. Transportation Research Part F: Traffic Psychology and Behaviour, 15(4), 462–470. https://doi.org/10.1016/j.trf.2011.10.001
   Web of Science ®Google Scholar
• Eckersley, A., & Butters, L. (2014). The Influence of modern day tractor cab ergonomics on productivity. Scientific Papers-Series A, Agronomy, 57, 389–394.
   Google Scholar
• El Haouij, N., Poggi, J. M., Ghozi, R., Sevestre-Ghalila, S., & Jaïdane, M. (2019). Random forest-based approach for physiological functional variable selection for driver’s stress level classification. Statistical Methods & Applications, 28(1), 157–185. https://doi.org/10.1007/s10260-018-0423-5
   Web of Science ®Google Scholar
• Engström, J., Johansson, E., & Östlund, J. (2005). Effects of visual and cognitive load in real and simulated motorway driving. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 97–120. https://doi.org/10.1016/j.trf.2005.04.012
   Web of Science ®Google Scholar
• Faure, V., Lobjois, R., & Benguigui, N. (2016). The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 78–90. https://doi.org/10.1016/j.trf.2016.04.007
   Web of Science ®Google Scholar
• Feng, F., Liu, Y., & Chen, Y. (2018). Effects of quantity and size of buttons of in-vehicle touch screen on drivers’ eye glance behavior. International Journal of Human–Computer Interaction, 34(12), 1105–1118. https://doi.org/10.1080/10447318.2017.1415688
   Web of Science ®Google Scholar
• Feng, Z., Yang, M., Du, Y., Xu, J., Huang, C., & Jiang, X. (2021). Effects of the spatial structure conditions of urban underpass tunnels’ longitudinal section on drivers’ physiological and behavioral comfort. International Journal of Environmental Research and Public Health, 18(20), 10992. https://doi.org/10.3390/ijerph182010992
   PubMed Web of Science ®Google Scholar
• Figueroa-Medina, A. M. (2005). Subjective and objective risks consideration in modeling highway safety. Purdue University.
   Google Scholar
• Figueroa-Medina, A. M., & Tarko, A. P. (2007). Speed changes in the vicinity of horizontal curves on two-lane rural roads. Journal of Transportation Engineering, 133(4), 215–222. https://doi.org/10.1061/(ASCE)0733-947X(2007)133:4(215)
   Google Scholar
• Funazaki, Y., Seto, N., Ninomiya, K., Hikawa, K., Nakamura, S., & Yamanaka, S. (2022). Driving experiment system using HMDs to measure drivers’ proficiency and difficulty of various road conditions. In H. Krömker (Ed.), International conference on human-computer interaction (pp. 247–257). Springer. https://doi.org/10.1007/978-3-031-04987-3_17
   Google Scholar
• Gerber, J. M., & Joubert, J. W. (2022). Impact of road grade on the risk profile of driver behavior. Transportation Research Record, 2677(2), 235–245. https://doi.org/10.1177/03611981221089939
   Web of Science ®Google Scholar
• Gorucu, S., Murphy, D., Foster, D., Hanagriff, R., & Ewing, J. (2018). Technology use among youth while operating farm equipment. Journal of Agromedicine, 23(4), 305–314. https://doi.org/10.1080/1059924X.2018.1501450
   PubMed Web of Science ®Google Scholar
• Graichen, L., Graichen, M., & Krems, J. F. (2019). Evaluation of gesture-based in-vehicle interaction: User experience and the potential to reduce driver distraction. Human Factors: The Journal of the Human Factors and Ergonomics Society, 61(5), 774–792. https://doi.org/10.1177/0018720818824253
   PubMed Web of Science ®Google Scholar
• Guo, B., Hua, Q., Jin, L., Xie, X., Huo, Z., & Wang, H. (2022). Analysis of driving control characteristics in typical road types. Sustainability, 14(2), 782. https://doi.org/10.3390/su14020782
   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 P. A. Hancock & N. Meshkati (Eds.), Advances in psychology (Vol. 52, pp. 139–183). North-Holland. https://doi.org/10.1016/S0166-4115(08)62386-9
   Google Scholar
• Jeong, H., & Liu, Y. (2019). Effects of non-driving-related-task modality and road geometry on eye movements, lane-keeping performance, and workload while driving. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 157–171. https://doi.org/10.1016/j.trf.2018.10.015
   Web of Science ®Google Scholar
• Johnson, M. J., Chahal, T., Stinchcombe, A., Mullen, N., Weaver, B., & Bédard, M. (2011). Physiological responses to simulated and on-road driving. International Journal of Psychophysiology, 81(3), 203–208. https://doi.org/10.1016/j.ijpsycho.2011.06.012
   PubMed Web of Science ®Google Scholar
• Jung, S., Park, J., Park, J., Choe, M., Kim, T., Choi, M., & Lee, S. (2021). Effect of touch button interface on in-vehicle information systems usability. International Journal of Human–Computer Interaction, 37(15), 1404–1422. https://doi.org/10.1080/10447318.2021.1886484
   Web of Science ®Google Scholar
• Kabir, M. S. N., Chung, S. O., Kim, Y. J., Sung, N. S., & Hong, S. J. (2017). Measurement and evaluation of whole body vibration of agricultural tractor operator. International Journal of Agricultural and Biological Engineering, 10(1), 248–255. https://doi.org/10.3965/j.ijabe.20171001.2113
   Web of Science ®Google Scholar
• Karimi, D., Mondor, T. A., & Mann, D. D. (2008). Application of auditory signals to the operation of an agricultural vehicle: Results of pilot testing. Journal of Agricultural Safety and Health, 14(1), 71–78. https://doi.org/10.13031/2013.24124
   PubMedGoogle Scholar
• Kim, H. S., Hwang, Y., Yoon, D., Choi, W., & Park, C. H. (2014). Driver workload characteristics analysis using EEG data from an urban road. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1844–1849. https://doi.org/10.1109/TITS.2014.2333750
   Web of Science ®Google Scholar
• Kim, J., Kim, W., Kim, H. S., Yoon, D. (2020). A study on the correlation between subjective driver readiness and NDRT Type during automated driving. In 2020 international conference on information and communication technology convergence (ICTC) (pp. 1774–1776). IEEE. https://doi.org/10.1109/ICTC49870.2020.9289293
   Google Scholar
• Kim, M. H., & Son, J. (2011). On-road assessment of in-vehicle driving workload for older drivers: Design guidelines for intelligent vehicles. International Journal of Automotive Technology, 12(2), 65–272. https://doi.org/10.1007/s12239-011-0031-y
   Web of Science ®Google Scholar
• Kim, N., Choe, M., Park, J., Park, J., Kim, H. K., Kim, J., Hussain, M., & Jung, S. (2021). Analysis of relationship between electroencephalograms and subjective measurements for in-vehicle information system: A preliminary study. International Journal of Environmental Research and Public Health, 18(22), 12173. https://doi.org/10.3390/ijerph182212173
   PubMed Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Koohestani, A., Kebria, P. M., Khosravi, A., Nahavandi, S. (2019). Drivers awareness evaluation using physiological measurement in a driving simulator. In 2019 IEEE international conference on industrial technology (ICIT) (pp. 859–864). IEEE. https://doi.org/10.1109/ICIT.2019.8755188
   Google Scholar
• Lansdown, T. C. (2012). Individual differences and propensity to engage with in-vehicle distractions–A self-report survey. Transportation Research Part F: Traffic Psychology and Behaviour, 15(1), 1–8. https://doi.org/10.1016/j.trf.2011.09.001
   Web of Science ®Google Scholar
• Lee, J., Kishino, S., & Suzuki, K. (2020). Riding performance quantification method for motorcycles in terms of collision probability using the logit model. Mechanical Engineering Journal, 7(4), 20–00015. https://doi.org/10.1299/mej.20-00015
   Web of Science ®Google Scholar
• Lehtonen, E., Lappi, O., & Summala, H. (2012). Anticipatory eye movements when approaching a curve on a rural road depend on working memory load. Transportation Research Part F: Traffic Psychology and Behaviour, 15(3), 369–377. https://doi.org/10.1016/j.trf.2011.08.007
   Web of Science ®Google Scholar
• Li, N., & Boyle, L. N. (2020). Allocation of driver attention for varying in-vehicle system modalities. Human Factors: The Journal of the Human Factors and Ergonomics Society, 62(8), 1349–1364. https://doi.org/10.1177/0018720819879585
   PubMed Web of Science ®Google Scholar
• Li, X., Schroeter, R., Rakotonirainy, A., Kuo, J., & Lenné, M. G. (2021). Get ready for take-overs: using head-up display for drivers to engage in non–driving-related tasks in automated vehicles. Human Factors: The Journal of the Human Factors and Ergonomics Society, 00187208211056200. https://doi.org/10.1177/00187208211056200
   Web of Science ®Google Scholar
• Li, X., Yan, X., & Wong, S. C. (2015). Effects of fog, driver experience and gender on driving behavior on S-curved road segments. Accident Analysis & Prevention, 77, 91–104. https://doi.org/10.1016/j.aap.2015.01.022
   PubMed Web of Science ®Google Scholar
• Li, Y. P., Cao, Z., Feng, L., & Yang, Z. G. (2013). Road traffic crash accident distribution law analysis in China. In Applied mechanics and materials (Vol. 253, pp. 1611–1614). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMM.253-255.1611
   Google Scholar
• Ma, J., Li, J., & Gong, Z. (2022). Evaluation of driver distraction from in-vehicle information systems: A simulator study of interaction modes and secondary tasks classes on eight production cars. International Journal of Industrial Ergonomics, 92, 103380. https://doi.org/10.1016/j.ergon.2022.103380
   Web of Science ®Google Scholar
• Mantzke, O., & Keinath, A. (2015). Relating the detection response task to critical events–Consequences of high cognitive workload to brake reaction times. Procedia Manufacturing, 3, 2381–2386. https://doi.org/10.1016/j.promfg.2015.07.386
   Google Scholar
• McNally, B., & Bradley, G. L. (2014). Re-conceptualising the reckless driving behavior of young drivers. Accident Analysis & Prevention, 70, 245–257. https://doi.org/10.1016/j.aap.2014.04.014
   PubMed Web of Science ®Google Scholar
• Metz, B., Schoch, S., Just, M., & Kuhn, F. (2014). How do drivers interact with navigation systems in real life conditions?: Results of a field-operational-test on navigation systems. Transportation Research Part F: Traffic Psychology and Behaviour, 24, 146–157. https://doi.org/10.1016/j.trf.2014.04.011
   Web of Science ®Google Scholar
• Meuleners, L., & Fraser, M. (2015). A validation study of driving errors using a driving simulator. Transportation Research Part F: Traffic Psychology and Behaviour, 29, 14–21. https://doi.org/10.1016/j.trf.2014.11.009
   Web of Science ®Google Scholar
• Minusa, S., Mizuno, K., Ojiro, D., Tanaka, T., Kuriyama, H., Yamano, E., Kuratsune, H., & Watanabe, Y. (2021). Increase in rear-end collision risk by acute stress-induced fatigue in on-road truck driving. PLoS One. 16(10), e0258892. https://doi.org/10.1371/journal.pone.0258892
   PubMed Web of Science ®Google Scholar
• Najib, L., Abdullah, L., Abdullah, I., & Salleh, Z. (2012). Weights of road accident causes using analytic hierarchy process. ARPN Journal of Science and Technology, 2(2), 39–44.
   Google Scholar
• Neyens, D. M., & Boyle, L. N. (2007). The effect of distractions on the crash types of teenage drivers. Accident Analysis & Prevention, 39(1), 206–212. https://doi.org/10.1016/j.aap.2006.07.004
   PubMed Web of Science ®Google Scholar
• Niu, J., Wang, X., Liu, X., Wang, D., Qin, H., & Zhang, Y. (2019). Effects of mobile phone use on driving performance in a multiresource workload scenario. Traffic Injury Prevention, 20(1), 37–44. https://doi.org/10.1080/15389588.2018.1527468
   PubMed Web of Science ®Google Scholar
• Onate-Vega, D., Oviedo-Trespalacios, O., & King, M. J. (2020). How drivers adapt their behavior to changes in task complexity: The role of secondary task demands and road environment factors. Transportation Research Part F: Traffic Psychology and Behavior, 71, 145–156. https://doi.org/10.1016/j.trf.2020.03.015
   Web of Science ®Google Scholar
• Oviedo-Trespalacios, O., Haque, M. M., King, M., & Washington, S. (2017). Effects of road infrastructure and traffic complexity in speed adaptation behavior of distracted drivers. Accident Analysis & Prevention, 101, 67–77. https://doi.org/10.1016/j.aap.2017.01.018
   PubMed Web of Science ®Google Scholar
• Oviedo-Trespalacios, O., Nandavar, S., & Haworth, N. (2019). How do perceptions of risk and other psychological factors influence the use of in-vehicle information systems (IVIS)? Transportation Research Part F: Traffic Psychology and Behavior, 67, 113–122. https://doi.org/10.1016/j.trf.2019.10.011
   Web of Science ®Google Scholar
• Park, M. S., Fukuda, T., Kim, T. G., & Maeda, S. (2013). Health risk evaluation of whole-body vibration by ISO 2631-5 and ISO 2631-1 for operators of agricultural tractors and recreational vehicles. Industrial Health, 51(3), 364–370. https://doi.org/10.2486/indhealth.2012-0045
   Web of Science ®Google Scholar
• Perello-March, J. R., Burns, C. G., Woodman, R., Elliott, M. T., & Birrell, S. A. (2021). Driver state monitoring: Manipulating reliability expectations in simulated automated driving scenarios. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5187–5197. https://doi.org/10.1109/TITS.2021.3050518
   Web of Science ®Google Scholar
• Qin, L., & Hamdar, S. H. (2015). Weather and road geometry impact on acceleration behavior: Experimental set-up and data collection using a driving simulator. In M. Chraibi, M. Boltes, A. Schadschneider, & A. Seyfried (Eds.), Traffic and granular flow’13 (pp. 513–523). Springer. https://doi.org/10.1007/978-3-319-10629-8_57
   Google Scholar
• Radhakrishnan, V., Merat, N., Louw, T., Gonçalves, R. C., Torrao, G., Lyu, W., Guillen, P. P., & Lenné, M. G. (2022). Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving. Transportation Research Part F: Traffic Psychology and Behavior, 87, 149–163. https://doi.org/10.1016/j.trf.2022.04.002
   Web of Science ®Google Scholar
• Reyes, M. L., & Lee, J. D. (2008). Effects of cognitive load presence and duration on driver eye movements and event detection performance. Transportation Research Part F: Traffic Psychology and Behavior, 11(6), 391–402. https://doi.org/10.1016/j.trf.2008.03.004
   Web of Science ®Google Scholar
• Roman, G. D., Poulter, D., Barker, E., McKenna, F. P., & Rowe, R. (2015). Novice drivers’ individual trajectories of driver behavior over the first three years of driving. Accident Analysis & Prevention, 82, 61–69. https://doi.org/10.1016/j.aap.2015.05.012
   PubMed Web of Science ®Google Scholar
• Sadia, R., Bekhor, S., & Polus, A. (2018). Speed variation for different drivers, situations, and road geometry: Simulator and survey analysis. Journal of Transportation Safety & Security, 10(1–2), 25–44. https://doi.org/10.1080/19439962.2016.1275071
   Web of Science ®Google Scholar
• Sheng, L. I. N., Jian-Bei, L. I. U., Ying, Y. A. N., Jing, L. U. O., & Zhen-Zhou, Y. U. A. N. (2013). Safety evaluation model of long-steep downgrade section for montane highway based on driving workload. Journal of Traffic and Transportation Engineering, 13(6), 99–106.
   Google Scholar
• Skottke, E. M., Debus, G., Wang, L., & Huestegge, L. (2014). Carryover effects of highly automated convoy driving on subsequent manual driving performance. Human Factors, 56(7), 1272–1283. https://doi.org/10.1177/0018720814524594
   PubMed Web of Science ®Google Scholar
• Stapel, J., Mullakkal-Babu, F. A., & Happee, R. (2019). Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving. Transportation Research Part F: traffic Psychology and Behavior, 60, 590–605. https://doi.org/10.1016/j.trf.2018.11.006
   Web of Science ®Google Scholar
• Strayer, D. L., Cooper, J. M., McCarty, M. M., Getty, D. J., Wheatley, C. L., Motzkus, C. J., Goethe, R. M., Biondi, F., & Horrey, W. J. (2019). Visual and cognitive demands of carplay, android auto, and five native infotainment systems. Human Factors, 61(8), 1371–1386. https://doi.org/10.1177/0018720819836575
   PubMed Web of Science ®Google Scholar
• Sundfør, H. B., Sagberg, F., & Høye, A. (2019). Inattention and distraction in fatal road crashes–Results from in-depth crash investigations in Norway. Accident Analysis & Prevention, 125, 152–157. https://doi.org/10.1016/j.aap.2019.02.004
   PubMed Web of Science ®Google Scholar
• Victor, T., Dozza, M., Bärgman, J., Boda, C. N., Engström, J., Flannagan, C., Lee, J. D., & Markkula, G. (2015). Analysis of naturalistic driving study data: Safer glances, driver inattention, and crash risk (No. SHRP 2 Report S2-S08A-RW-1). The National Academies Press. https://doi.org/10.17226/22297
   Google Scholar
• Wan, J., & Wu, C. (2018). The effects of vibration patterns of take-over request and non-driving tasks on taking-over control of automated vehicles. International Journal of Human–Computer Interaction, 34(11), 987–998. https://doi.org/10.1080/10447318.2017.1404778
   Web of Science ®Google Scholar
• Wang, K., Zhang, W., Feng, Z., Sze, N. N., Hu, Z., & Wang, C. (2020). Maximum acceptable speed based on physiological performance for different alignments of rural roads under the influence of visibility in fog—A driving simulator study. Traffic Injury Prevention, 21(2), 145–150. https://doi.org/10.1080/15389588.2020.1724978
   PubMed Web of Science ®Google Scholar
• Xie, L., Wu, C., Duan, M., & Lyu, N. (2021). Analysis of freeway safety influencing factors on driving workload and performance based on the gray correlation method. Journal of Advanced Transportation, 2021. https://doi.org/10.1155/2021/6566207
   Web of Science ®Google Scholar
• Xie, L., Wu, C., Lyu, N., & Duan, Z. (2019). Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance. Advances in Mechanical Engineering, 11(5), 1687814019853690. https://doi.org/10.1177/1687814019853925
   Web of Science ®Google Scholar
• Yan, W., Wong, S. C., Loo, B. P. Y., Wu, C. Y. H., Huang, H., Pei, X., & Meng, F. (2022). An assessment of the effect of green signal countdown timers on drivers’ behavior and on road safety at intersections, based on driving simulator experiments and naturalistic observation studies. Journal of Safety Research, 82, 1–12. https://doi.org/10.1016/j.jsr.2022.04.001
   Web of Science ®Google Scholar
• Zhang, Y., Gao, P., & Ahamed, T. (2018). Development of a rescue system for agricultural machinery operators using machine vision. Biosystems Engineering, 169, 149–164. https://doi.org/10.1016/j.biosystemseng.2018.02.009
   Web of Science ®Google Scholar
• Zhao, J., & Mao, E. (2022). Emerging automated technologies on tractors. In S. Ma, T. Lin, E. Mao, Z. Song, & K. C. Ting (Eds.), Sensing, data managing, and control technologies for agricultural systems (pp. 203–222). Springer. https://doi.org/10.1007/978-3-031-03834-1_9
   Google Scholar
• Zhao, J. Y., He, C., & Zheng, M. M. (2010). Effect of longitudinal slope of tunnel of freeway on heart beat of drivers. Journal of Chang’an University (Natural Science Edition), 30(2), 80–83. https://doi.org/10.19721/j.cnki.1671-8879.2010.02.017
   Google Scholar
• Zhao, X., Du, Y., Mao, E., Zhu, Z., & Song, Z. (2022). Agricultural tractor dynamics simulation research based on a driving simulator with real-time human-machine interaction. Computers and Electronics in Agriculture, 203, 107443. https://doi.org/10.1016/j.compag.2022.107443
   Web of Science ®Google Scholar
• Ziakopoulos, A. (2021). Spatial analysis of harsh driving behavior events in urban networks using high-resolution smartphone and geometric data. Accident; Analysis and Prevention, 157, 106189. https://doi.org/10.1016/j.aap.2021.106189
   PubMed Web of Science ®Google Scholar