From Distraction to Action: Elevating Situation Awareness with Visual Assistance in Level 3 Autonomous Driving

References

• Banks, V. A., & Stanton, N. A. (2016). Keep the driver in control: Automating automobiles of the future. Applied Ergonomics, 53 Pt B, 389–395. https://doi.org/10.1016/j.apergo.2015.06.020
   PubMed Web of Science ®Google Scholar
• Carsten, O., Lai, F. C., Barnard, Y., Jamson, A. H., & Merat, N. (2012). Control task substitution in semiautomated driving: Does it matter what aspects are automated? Human Factors, 54(5), 747–761. https://doi.org/10.1177/0018720812460246
   PubMed Web of Science ®Google Scholar
• Cao, A., Chintamani, K. K., Pandya, A. K., & Ellis, R. D. (2009). NASA TLX: Software for assessing subjective mental workload. Behavior Research Methods, 41(1), 113–117. https://doi.org/10.3758/BRM.41.1.113
   PubMed Web of Science ®Google Scholar
• Cohen-Lazry, G., Borowsky, A., & Oron-Gilad, T. (2020). The impact of auditory continual feedback on take-overs in Level 3 automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 75, 145–159. https://doi.org/10.1016/j.trf.2020.10.003
   Web of Science ®Google Scholar
• Dillmann, J., Den Hartigh, R. J. R., Kurpiers, C. M., Raisch, F. K., De Waard, D., & Cox, R. F. A. (2021). Keeping the driver in the loop in conditionally automated driving: A perception-action theory approach. Transportation Research Part F: Traffic Psychology and Behaviour, 79, 49–62. https://doi.org/10.1016/j.trf.2021.03.003
   Web of Science ®Google Scholar
• Endsley, M. R. (2021). Situation awareness. In Handbook of human factors and ergonomics (pp. 434–455). John Wiley & Sons Inc.
   Google Scholar
• Endsley, M. R. (1995). Measurement of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 65–84. https://doi.org/10.1518/001872095779049499
   Web of Science ®Google Scholar
• Endsley, M. R. (1988). Design and evaluation for situation awareness enhancement. Proceedings of the Human Factors Society Annual Meeting, 32(2), 97–101. https://doi.org/10.1177/154193128803200221
   Google Scholar
• Eriksson, A., & Stanton, N. A. (2017). Takeover time in highly automated vehicles: Noncritical transitions to and from manual control. Human Factors, 59(4), 689–705. https://doi.org/10.1177/0018720816685832
   PubMed Web of Science ®Google Scholar
• Feinauer, S., Schuller, L., Groh, I., Huestegge, L., & Petzoldt, T. (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. https://doi.org/10.1016/j.trf.2022.08.009
   Web of Science ®Google Scholar
• Gold, C., Körber, M., Lechner, D., & Bengler, K. (2016). Taking over control from highly automated vehicles in complex traffic situations: The role of traffic density. Human Factors, 58(4), 642–652. https://doi.org/10.1177/0018720816634226
   PubMed Web of Science ®Google Scholar
• Gruden, T., Tomažič, S., Sodnik, J., & Jakus, G. (2022). A user study of directional tactile and auditory user interfaces for take-over requests in conditionally automated vehicles. Accident; Analysis and Prevention, 174, 106766. https://doi.org/10.1016/j.aap.2022.106766
   PubMed Web of Science ®Google Scholar
• Gruyer, D., Magnier, V., Hamdi, K., Claussmann, L., Orfila, O., & Rakotonirainy, A. (2017). Perception, information processing and modeling: Critical stages for autonomous driving applications. Annual Reviews in Control, 44, 323–341. https://doi.org/10.1016/j.arcontrol.2017.09.012
   Web of Science ®Google Scholar
• Hungund, A. P., & Pradhan, A. K. (2023). Impact of non-driving related tasks while operating automated driving systems (ADS): A systematic review. Accident; Analysis and Prevention, 188, 107076. https://doi.org/10.1016/j.aap.2023.107076
   PubMed Web of Science ®Google Scholar
• Lee, S. C., Yoon, S. H., & Ji, Y. G. (2021). Effects of non-driving-related task attributes on takeover quality in automated vehicles. International Journal of Human–Computer Interaction, 37(3), 211–219. https://doi.org/10.1080/10447318.2020.1815361
   Web of Science ®Google Scholar
• Li, M., Feng, Z., Zhang, W., Wang, L., Wei, L., & Wang, C. (2023). How much situation awareness does the driver have when driving autonomously? A study based on driver attention allocation. Transportation Research Part C: Emerging Technologies, 156, 104324. https://doi.org/10.1016/j.trc.2023.104324
   Web of Science ®Google Scholar
• Li, X., Schroeter, R., Rakotonirainy, A., Kuo, J., & Lenné, M. G. (2020). Effects of different non-driving-related-task display modes on drivers’ eye-movement patterns during take-over in an automated vehicle. Transportation Research Part F: Traffic Psychology and Behaviour, 70, 135–148. https://doi.org/10.1016/j.trf.2020.03.001
   Web of Science ®Google Scholar
• Liang, N., Yang, J., Yu, D., Prakah-Asante, K. O., Curry, R., Blommer, M., Swaminathan, R., & Pitts, B. J. (2021). Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving. Accident; Analysis and Prevention, 157, 106143. https://doi.org/10.1016/j.aap.2021.106143
   PubMed Web of Science ®Google Scholar
• Lindgren, T., Fors, V., Pink, S., & Osz, K. (2020). Anticipatory experience in everyday autonomous driving. Personal and Ubiquitous Computing, 24(6), 747–762. https://doi.org/10.1007/s00779-020-01410-6
   Web of Science ®Google Scholar
• Liu, W., Zhu, Y., Huang, R., Ohashi, T., Auernhammer, J., Zhang, X., Shi, C., & Wang, L. (2022). Designing interactive glazing through an engineering psychology approach: Six augmented reality scenarios that envision future car human-machine interface. Virtual Reality & Intelligent Hardware, 5(2), 157–170. https://doi.org/10.1016/j.vrih.2022.07.004
   Google Scholar
• Lu, Z., Zhang, B., Feldhütter, A., Happee, R., Martens, M., & De Winter, J. C. (2019). Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance. Transportation Research Part F: Traffic Psychology and Behaviour, 63, 22–37. https://doi.org/10.1016/j.trf.2019.03.018
   Web of Science ®Google Scholar
• Mathôt, S., Fabius, J., Van Heusden, E., & van der Stigchel, S. (2018). Safe and sensible preprocessing and baseline correction of pupil-size data. Behavior Research Methods, 50(1), 94–106. https://doi.org/10.3758/s13428-017-1007-2
   PubMed Web of Science ®Google Scholar
• Matthews, G., Neubauer, C., Saxby, D. J., Wohleber, R. W., & Lin, J. (2019). Dangerous intersections? A review of studies of fatigue and distraction in the automated vehicle. Accident; Analysis and Prevention, 126, 85–94. https://doi.org/10.1016/j.aap.2018.04.004
   PubMed Web of Science ®Google Scholar
• Merat, N., Jamson, A. H., Lai, F. C., & Carsten, O. (2012). Highly automated driving, secondary task performance, and driver state. Human Factors, 54(5), 762–771. https://doi.org/10.1177/0018720812442087
   PubMed Web of Science ®Google Scholar
• McCall, R., McGee, F., Mirnig, A., Meschtscherjakov, A., Louveton, N., Engel, T., & Tscheligi, M. (2019). A taxonomy of autonomous vehicle handover situations. Transportation Research Part A: Policy and Practice, 124, 507–522. https://doi.org/10.1016/j.tra.2018.05.005
   Web of Science ®Google Scholar
• Naujoks, F., Höfling, S., Purucker, C., & Zeeb, K. (2018). From partial and high automation to manual driving: Relationship between non-driving related tasks, drowsiness and take-over performance. Accident; Analysis and Prevention, 121, 28–42. https://doi.org/10.1016/j.aap.2018.08.018
   PubMed Web of Science ®Google Scholar
• Rauffet, P., Botzer, A., Chauvin, C., Saïd, F., & Tordet, C. (2020). The relationship between level of engagement in a non-driving task and driver response time when taking control of an automated vehicle. Cognition, Technology & Work, 22(4), 721–731. https://doi.org/10.1007/s10111-019-00611-1
   Web of Science ®Google Scholar
• Roche, F., Somieski, A., & Brandenburg, S. (2019). Behavioral changes to repeated takeovers in highly automated driving: Effects of the takeover-request design and the nondriving-related task modality. Human Factors, 61(5), 839–849. https://doi.org/10.1177/0018720818814963
   PubMed Web of Science ®Google Scholar
• SAE On-Road Automated Vehicle Standards Committee. (2014). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Standard Journal 3016, 1.
   Google Scholar
• Sahaï, A., Barré, J., & Bueno, M. (2021). Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes. Transportation Research Part F: Traffic Psychology and Behaviour, 81, 130–143. https://doi.org/10.1016/j.trf.2021.06.001
   Web of Science ®Google Scholar
• Shi, J., Wu, C., Zheng, H., Zhang, W., Zhang, X., Lu, P., & Chai, C. (2023). Toward hazard or action? Effects of directional vibrotactile takeover requests on takeover performance in automated driving. International Journal of Human–Computer Interaction, 39(19), 3786–3801. https://doi.org/10.1080/10447318.2022.2105479
   Web of Science ®Google Scholar
• Soares, S., Lobo, A., Ferreira, S., Cunha, L., & Couto, A. (2021). Takeover performance evaluation using driving simulation: A systematic review and meta-analysis. European Transport Research Review, 13(1), 18. https://doi.org/10.1186/s12544-021-00505-2
   Web of Science ®Google Scholar
• Stanton, N. A., Salmon, P. M., Walker, G. H., & Jenkins, D. P. (2010). Is situation awareness all in the mind? Theoretical Issues in Ergonomics Science, 11(1–2), 29–40. https://doi.org/10.1080/14639220903009938
   Google Scholar
• Stephanidis, C., Salvendy, G., Akoumianakis, D., Arnold, A., Bevan, N., Dardailler, D., Emiliani, P. L., Iakovidis, I., Jenkins, P., Karshmer, A., Korn, P., Marcus, A., Murphy, H., Oppermann, C., Stary, C., Tamura, H., Tscheligi, M., Ueda, H., Weber, G., & Ziegler, J. (1999). Toward an information society for all: HCI challenges and R&D recommendations. International Journal of Human-Computer Interaction, 11(1), 1–28. https://doi.org/10.1207/s15327590ijhc1101_1
   Web of Science ®Google Scholar
• Tan, H., Sun, J., Wenjia, W., & Zhu, C. (2021). User experience & usability of driving: A bibliometric analysis of 2000–2019. International Journal of Human–Computer Interaction, 37(4), 297–307. https://doi.org/10.1080/10447318.2020.1860516
   Web of Science ®Google Scholar
• Vansteenkiste, P., Cardon, G., Philippaerts, R., & Lenoir, M. (2015). Measuring dwell time percentage from head-mounted eye-tracking data–comparison of a frame-by-frame and a fixation-by-fixation analysis. Ergonomics, 58(5), 712–721. https://doi.org/10.1080/00140139.2014.990524
   PubMed Web of Science ®Google Scholar
• Vlakveld, W., van Nes, N., de Bruin, J., Vissers, L., & van der Kroft, M. (2018). Situation awareness increases when drivers have more time to take over the wheel in a Level 3 automated car: A simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 58, 917–929. https://doi.org/10.1016/j.trf.2018.07.025
   Web of Science ®Google Scholar
• Vogelpohl, T., Gehlmann, F., & Vollrath, M. (2020). Task interruption and control recovery strategies after take-over requests emphasize need for measures of situation awareness. Human Factors, 62(7), 1190–1211. https://doi.org/10.1177/0018720819866976
   PubMed Web of Science ®Google Scholar
• Walker, F., Wang, J., Martens, M. H., & Verwey, W. B. (2019). Gaze behaviour and electrodermal activity: Objective measures of drivers’ trust in automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 64, 401–412. https://doi.org/10.1016/j.trf.2019.05.021
   Web of Science ®Google Scholar
• Wandtner, B., Schömig, N., & Schmidt, G. (2018). Secondary task engagement and disengagement in the context of highly automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 58, 253–263. https://doi.org/10.1016/j.trf.2018.06.001
   Web of Science ®Google Scholar
• Weigl, K., Schartmüller, C., Riener, A., & Steinhauser, M. (2021). Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): Data-driven models for Level 3 and Level 5 automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 83, 42–59. https://doi.org/10.1016/j.trf.2021.09.011
   Web of Science ®Google Scholar
• Wintersberger, P., Schartmüller, C., Sadeghian, S., Frison, A. K., & Riener, A. (2021). Evaluation of imminent take-over requests with real automation on a test track. Human Factors, 65(8), 1776–1792. https://doi.org/10.1177/00187208211051435
   PubMed Web of Science ®Google Scholar
• Wu, Z., Zhao, L., Liu, G., Chai, J., Huang, J., & Ai, X. (2023). The effect of AR-HUD takeover assistance types on driver situation awareness in highly automated driving: A 360-degree panorama experiment. International Journal of Human–Computer Interaction, 1–18. https://doi.org/10.1080/10447318.2023.2254645
   Web of Science ®Google Scholar
• Xu, W., Dainoff, M. J., Ge, L., & Gao, Z. (2023). Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI. International Journal of Human–Computer Interaction, 39(3), 494–518. https://doi.org/10.1080/10447318.2022.2041900
   Web of Science ®Google Scholar
• Zeeb, K., Buchner, A., & Schrauf, M. (2015). What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accident; Analysis and Prevention, 78, 212–221. https://doi.org/10.1016/j.aap.2015.02.023
   PubMed Web of Science ®Google Scholar
• Zhang, T., Yang, J., Liang, N., Pitts, B. J., Prakah-Asante, K., Curry, R., Duerstock, B., Wachs, J. P., & Yu, D. (2023). Physiological measurements of situation awareness: A systematic review. Human Factors, 65(5), 737–758. https://doi.org/10.1177/0018720820969071
   PubMedGoogle Scholar
• Zhu, Y., Tang, G., Liu, W., & Qi, R. (2022). How post 90’s gesture interact with automobile skylight. International Journal of Human–Computer Interaction, 38(5), 395–405. https://doi.org/10.1080/10447318.2021.1948687
   Web of Science ®Google Scholar