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Age-Related Differences in Takeover Request Modality Preferences and Attention Allocation During Semi-autonomous Driving

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Human Aspects of IT for the Aged Population. Technologies, Design and User Experience (HCII 2020)

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Abstract

Adults aged 65 years and older are the fastest growing age group worldwide. Future autonomous vehicles may help to support the mobility of older individuals; however, these cars will not be widely available for several decades and current semi-autonomous vehicles often require manual takeover in unusual driving conditions. In these situations, the vehicle issues a takeover request in any uni-, bi- or trimodal combination of visual, auditory, or tactile alerts to signify the need for manual intervention. However, to date, it is not clear whether age-related differences exist in the perceived ease of detecting these alerts. Also, the extent to which engagement in non-driving-related tasks affects this perception in younger and older drivers is not known. Therefore, the goal of this study was to examine the effects of age on the ease of perceiving takeover requests in different sensory channels and on attention allocation during conditional driving automation. Twenty-four younger and 24 older adults drove a simulated SAE Level 3 vehicle under three conditions: baseline, while performing a non-driving-related task, and while engaged in a driving-related task, and were asked to rate the ease of detecting uni-, bi- or trimodal combinations of visual, auditory, or tactile signals. Both age groups found the trimodal alert to be the easiest to detect. Also, older adults focused more on the road than the secondary task compared to younger drivers. Findings may inform the development of next-generation of autonomous vehicle systems to be safe for a wide range of age groups.

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References

  1. Favarò, F., Eurich, S., Nader, N.: Autonomous vehicles’ disengagements: trends, triggers, and regulatory limitations. Accid. Anal. Prev. 110, 136–148 (2018). https://doi.org/10.1016/j.aap.2017.11.001

    Article  Google Scholar 

  2. Wan, J., Wu, C.: The effects of lead time of take-over request and nondriving tasks on taking-over control of automated vehicles. IEEE Trans. Hum.-Mach. Syst. 48, 582–591 (2018). https://doi.org/10.1109/THMS.2018.2844251

    Article  Google Scholar 

  3. Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., Oluwatola, O.: Autonomous Vehicle Technology: A Guide for Policymakers. Rand Corporation (2014). https://doi.org/10.7249/rr443-2

  4. Bishop, R.: Intelligent vehicle applications worldwide. IEEE Intell. Syst. Their Appl. 15, 78–81 (2000). https://doi.org/10.1109/5254.820333

    Article  Google Scholar 

  5. Young, M.S., Stanton, N.A.: What’s skill got to do with it? Vehicle automation and driver mental workload. Ergonomics 50, 1324–1339 (2007). https://doi.org/10.1080/00140130701318855

    Article  Google Scholar 

  6. Saffarian, M., de Winter, J.C.F., Happee, R.: Automated driving: human-factors issues and design solutions. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 56, 2296–2300 (2012). https://doi.org/10.1177/1071181312561483

  7. Litman, T.: Autonomous Vehicle Implementation Predictions: Implications for Transport Planning (2019)

    Google Scholar 

  8. Erber, J.T.: Aging and older adulthood, p. 466. Hoboken, Wiley-Blackwell (2012)

    Google Scholar 

  9. Czaja, S.J., Boot, W.R., Charness, N., Rogers, W.A.: Designing for Older Adults: Principles and Creative Human Factors Approaches. CRC Press, Boca Raton (2019). https://doi.org/10.1201/b22189

    Book  Google Scholar 

  10. Anstey, K.J., Wood, J., Lord, S., Walker, J.G.: Cognitive, sensory and physical factors enabling driving safety in older adults. Clin. Psychol. Rev. 25, 45–65 (2005). https://doi.org/10.1016/j.cpr.2004.07.008

    Article  Google Scholar 

  11. Lemke, U.: The challenges of aging – sensory, cognitive, socio-emotional and health changes in old age. In: Hearing Care for Adults 2009—The Challenge of Aging. Proceedings of the 2nd International Adult Conference, pp. 33–43. Phonak AG, Stäfa (2009)

    Google Scholar 

  12. Eby, D., Molnar, L.J., Zhang, L., St Louis, R.M., Zanier, N., Kostyniuk, L.P.: Keeping older adults driving safely: a research synthesis of advanced in-vehicle technologies. AAA Foundation for Traffic Safety (2015)

    Google Scholar 

  13. Molnar, L.J., Eby, D.W., St Louis, R.M., Neumeyer, A.L.: Promising approaches for promoting lifelong community mobility, Ann Arbor (2007)

    Google Scholar 

  14. Hassan, H., King, M., Watt, K.: The perspectives of older drivers on the impact of feedback on their driving behaviours: a qualitative study. Transp. Res. Part F: Traffic Psychol. Behav. 28, 25–39 (2015). https://doi.org/10.1016/J.TRF.2014.11.003

    Article  Google Scholar 

  15. SAE International: Automated driving: levels of driving automation are defined in new SAE international standard J3016, no. 1. SAE International (2014)

    Google Scholar 

  16. Eriksson, A., Stanton, N.A.: takeover time in highly automated vehicles: noncritical transitions to and from manual control. Hum. Factors 59, 689–705 (2017). https://doi.org/10.1177/0018720816685832

    Article  Google Scholar 

  17. Llaneras, R.E., Salinger, J., Green, C.A.: Human factors issues associated with limited ability autonomous driving systems: Drivers’ allocation of visual attention to the forward roadway. In: Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, pp. 92–98 (2013). https://doi.org/10.17077/drivingassessment.1472

  18. Zhang, B., de Winter, J., Varotto, S., Happee, R., Martens, M.: Determinants of take-over time from automated driving: A meta-analysis of 129 studies. Transp. Res. Part F: Traffic Psychol. Behav. 64, 285–307 (2019). https://doi.org/10.1016/j.trf.2019.04.020

    Article  Google Scholar 

  19. Mcdonald, A., Alambeigi, H.: Towards computational simulations of behavior during automated driving take- overs : a review of the empirical and modeling literatures (2019)

    Google Scholar 

  20. National Highway Traffic Safety Administration: Automated Driving Systems 2.0: A Vision for Safety (2017)

    Google Scholar 

  21. Politis, I., Brewster, S., Pollick, F.: Using Multimodal Displays to Signify Critical Handovers of Control to Distracted Autonomous Car Drivers. Int. J. Mob. Hum. Comput. Interact. 9, 1–16 (2017). https://doi.org/10.1016/j.bbapap.2014.08.013

    Article  Google Scholar 

  22. Huang, G., Steele, C., Zhang, X., Pitts, B.J.: Multimodal cue combinations: a possible approach to designing in-vehicle takeover requests for semi-autonomous driving. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 63, 1739–1743 (2019)

    Article  Google Scholar 

  23. Yoon, S.H., Kim, Y.W., Ji, Y.G.: The effects of takeover request modalities on highly automated car control transitions. Accid. Anal. Prev. 123, 150–158 (2019). https://doi.org/10.1016/j.aap.2018.11.018

    Article  Google Scholar 

  24. Pitts, B.J., Sarter, N.: What you don’t notice can harm you: age-related differences in detecting concurrent visual, auditory, and tactile cues. Hum. Factors 60, 445–464 (2018). https://doi.org/10.1177/0018720818759102

    Article  Google Scholar 

  25. Baldwin, C.L.: Verbal collision avoidance messages during simulated driving: perceived urgency, alerting effectiveness and annoyance. Ergonomics 54, 328–337 (2011). https://doi.org/10.1080/00140139.2011.558634

    Article  Google Scholar 

  26. Edworthy, J., Stanton, N.: Human Factors in Auditory Warnings. Routledge, Abingdon (2019). https://doi.org/10.4324/9780429455742

    Book  Google Scholar 

  27. Venkatesh, V., Davis, F.D.: Theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204 (2000). https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  28. Lee, S.C., Kim, Y.W., Ji, Y.G.: Effects of visual complexity of in-vehicle information display: age-related differences in visual search task in the driving context. Appl. Ergon. 81, 102888 (2019). https://doi.org/10.1016/j.apergo.2019.102888

    Article  Google Scholar 

  29. Nasreddine, Z.S., et al.: The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53, 695–699 (2005). https://doi.org/10.1111/j.1532-5415.2005.53221.x

    Article  Google Scholar 

  30. Lundqvist, L.M., Eriksson, L.: Age, cognitive load, and multimodal effects on driver response to directional warning. Appl. Ergon. 76, 147–154 (2019). https://doi.org/10.1016/j.apergo.2019.01.002

    Article  Google Scholar 

  31. Petermeijer, S., Bazilinskyy, P., Bengler, K., de Winter, J.: Take-over again: investigating multimodal and directional TORs to get the driver back into the loop. Appl. Ergon. 62, 204–215 (2017). https://doi.org/10.1016/j.apergo.2017.02.023

    Article  Google Scholar 

  32. Politis, I., Brewster, S., Pollick, F.: Evaluating multimodal driver displays of varying urgency. In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2013. pp. 92–99 (2013). https://doi.org/10.1145/2516540.2516543

  33. Suied, C., Susini, P., McAdams, S.: Evaluating warning sound urgency with reaction times. J. Exp. Psychol. Appl. 14, 201–212 (2008). https://doi.org/10.1037/1076-898X.14.3.201

    Article  Google Scholar 

  34. Meng, A., Siren, A.: Cognitive problems, self-rated changes in driving skills, driving-related discomfort and self-regulation of driving in old drivers. Accid. Anal. Prev. 49, 322–329 (2012)

    Article  Google Scholar 

  35. Gwyther, H., Holland, C.: The effect of age, gender and attitudes on self-regulation in driving. Accid. Anal. Prev. 45, 19–28 (2012). https://doi.org/10.1016/j.aap.2011.11.022

    Article  Google Scholar 

  36. Molnar, L., Eby, D., Zhang, L., Zanier, N., Louis, R., Kostyniuk, L.: Self-regulation of driving by older adults: a synthesis of the literature and framework. Aging (Albany. NY) 20, 227–235 (2015)

    Google Scholar 

  37. Rovira, E., McLaughlin, A.C., Pak, R., High, L.: Looking for age differences in self-driving vehicles: examining the effects of automation reliability, driving risk, and physical impairment on trust. Front. Psychol. 10 (2019). https://doi.org/10.3389/fpsyg.2019.00800

  38. Abraham, H., Lee, C., Mehler, B., Reimer, B.: Autonomous Vehicles and Alternatives to Driving: Trust, Preferences, and Effects of Age Learning to Use Technology View project. In: Transportation Research Board 96th Annual Meeting (2017)

    Google Scholar 

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Acknowledgement

The authors would like to acknowledge the support of funds from the National Science Foundation (NSF grant #1755746; Program Manager: Dr. Ephraim Glinert).

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Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Gaojian Huang & Brandon Pitts

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  1. Gaojian Huang
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Correspondence to Gaojian Huang .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Qin Gao

  2. Chongqing University, Chongqing, China

    Jia Zhou

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Huang, G., Pitts, B. (2020). Age-Related Differences in Takeover Request Modality Preferences and Attention Allocation During Semi-autonomous Driving. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. Technologies, Design and User Experience. HCII 2020. Lecture Notes in Computer Science(), vol 12207. Springer, Cham. https://doi.org/10.1007/978-3-030-50252-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-50252-2_11

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