دورية أكاديمية

Assessing driver distraction from in-vehicle information system: an on-road study exploring the effects of input modalities and secondary task types.

التفاصيل البيبلوغرافية
العنوان: Assessing driver distraction from in-vehicle information system: an on-road study exploring the effects of input modalities and secondary task types.
المؤلفون: Zhong Q; Department of Industrial Design, School of Design, Southwest Jiaotong University, Chengdu, 611756, China. 18238836586@163.com., Zhi J; Department of Industrial Design, School of Design, Southwest Jiaotong University, Chengdu, 611756, China. zhijinyi@swjtu.edu.cn., Xu Y; Department of Industrial Design, School of Design, Southwest Jiaotong University, Chengdu, 611756, China., Gao P; Department of Industrial Design, School of Design, Southwest Jiaotong University, Chengdu, 611756, China., Feng S; Department of Industrial Design, School of Design, Southwest Jiaotong University, Chengdu, 611756, China.
المصدر: Scientific reports [Sci Rep] 2024 Aug 31; Vol. 14 (1), pp. 20289. Date of Electronic Publication: 2024 Aug 31.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Task Performance and Analysis* , Automobile Driving*, Humans ; Male ; Female ; Young Adult ; Adult ; Distracted Driving ; Attention/physiology ; Adolescent
مستخلص: In-vehicle information system (IVIS) use is prevalent among young adults. However, their interaction with IVIS needs to be better understood. Therefore, an on-road study aims to explore the effects of input modalities and secondary task types on young drivers' secondary task performance, driving performance, and visual glance behavior. A 2 × 4 within-subject design was undertaken. The independent variables are input modalities (auditory-speech and visual-manual) and secondary task types (calls, music, navigation, and radio). The dependent variables include secondary task performance (task completion time, number of errors, and SUS), driving performance (average speed, number of lane departure warnings, and NASA-TLX), and visual glance behavior (average glance duration, number of glances, total glance duration, and number of glances over 1.6 s). The statistical analysis result showed that the main effect of input modalities is significant, with more distraction during visual-manual than auditory-speech. The main impact of secondary task types was also substantial across most metrics, aside from average speed and average glance duration. Navigation and music were the most distracting, followed by calls, and radio came in last. The distracting effect of input modalities is relatively stable and generally not moderated by the secondary task types, except radio tasks. The findings practically benefit the driver-friendly human-machine interface design, preventing IVIS-related distraction.
(© 2024. The Author(s).)
References: Zhong, Q., Zhi, J. & Guo, G. Dynamic is optimal: Effect of three alternative auto-complete on the usability of in-vehicle dialing displays and driver distraction. Traffic Inj. Prev. 23, 51–56 (2022). (PMID: 3493744110.1080/15389588.2021.2010052)
Kim, J., Kim, S. & Nam, C. User resistance to acceptance of in-vehicle infotainment (IVI) systems. Telecomm. Policy. 40, 919–930 (2016). (PMID: 10.1016/j.telpol.2016.07.006)
Statista. Shipments of the in-vehicle IVISs worldwide from 2015 to 2022 (in million units). https://www.statista.com/statistics/784966/in-car-infotainment-systems-shipments-worldwide (2023).
Kim, G. Y., Kim, S. R., Kim, M. J., Shim, J. M. & Ji, Y. G. Effects of animated screen transition in in-vehicle infotainment systems: Perceived duration, delay time, and satisfaction. Int. J. Hum. Comput. Interact. 39, 203–216 (2023). (PMID: 10.1080/10447318.2022.2041886)
Lipovac, K., Deric, M., Tesic, M., Andric, Z. & Maric, B. Mobile phone use while driving-literary review. Transp. Res. Part F Traffic Psychol. Behav. 47, 132–142 (2017). (PMID: 10.1016/j.trf.2017.04.015)
Wang, L. & Ju, D. Y. Concurrent use of an in-vehicle navigation system and a smartphone navigation application. Soc. Behav. Pers. 43, 1629–1640 (2015). (PMID: 10.2224/sbp.2015.43.10.1629)
Oviedo-Trespalacios, O. Getting away with texting: Behavioural adaptation of drivers engaging in visual-manual tasks while driving. Transp. Res. Part A Policy Pract. 116, 112–121 (2018). (PMID: 10.1016/j.tra.2018.05.006)
Simmons, S. M., Caird, J. K. & Steel, P. A meta-analysis of in-vehicle and nomadic voice-recognition system interaction and driving performance. Accid. Anal. Prev. 106, 31–43 (2017). (PMID: 2855406310.1016/j.aap.2017.05.013)
Ma, Y. et al. Support vector machines for the identification of real-time driving distraction using in-vehicle information systems. J. Transp. Saf. Secur. 14, 232–255 (2022).
Kohl, J., Gross, A., Henning, M. & Baumgarten, T. Driver glance behavior towards displayed images on in-vehicle information systems under real driving conditions. Transp. Res. Part F Traffic Psychol. Behav. 70, 163–174 (2020). (PMID: 10.1016/j.trf.2020.01.017)
Ebel, P., Lingenfelder, C. & Vogelsang, A. On the forces of driver distraction: Explainable predictions for the visual demand of in-vehicle touchscreen interactions. Accid. Anal. Prev. 183, 106956 (2023). (PMID: 3668101710.1016/j.aap.2023.106956)
Peng, Y. & Boyle, L. N. Driver’s adaptive glance behavior to in-vehicle information systems. Accid. Anal. Prev. 85, 93–101 (2015). (PMID: 2640653810.1016/j.aap.2015.08.002)
Peng, Y., Boyle, L. N. & Lee, J. D. Reading, typing, and driving: How interactions with in-vehicle systems degrade driving performance. Transp. Res. Part F Traffic Psychol. Behav. 27, 182–191 (2014). (PMID: 10.1016/j.trf.2014.06.001)
Zhong, Q., Zhi, J. & Guo, G. Effect of the complexity of in-vehicle information interface on visual search and driving behavior. J. Saf. Environ. 22, 2003–2010 (2022).
Zhong, Q., Guo, G. & Zhi, J. Chinese handwriting while driving: Effects of handwritten box size on in-vehicle information systems usability and driver distraction.Traffic Inj. Prev. 24, 26–31 (2023).
Zhong, Q., Zhi, J. & Guo, G. Influence of in-vehicle information system interaction modes on driving behavior. J. Saf. Environ. 22, 1406–1411 (2022).
Strayer, D. L. et al. Assessing the visual and cognitive demands of in-vehicle information systems. Cogn. Res. Princ. Implic. 4, 1–22 (2019).
Strayer, D. L. et al. Visual and cognitive demands of CarPlay, Android Auto, and five native infotainment systems. Hum. Factors 61, 1371–1386 (2019). (PMID: 3095064510.1177/0018720819836575)
Dingus, T. A. et al. Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc. Natl. Acad. Sci. USA. 113, 2636–2641 (2016). (PMID: 26903657479099610.1073/pnas.1513271113)
Singh, H. & Kathuria, A. Analyzing driver behavior under naturalistic driving conditions: A review. Accid. Anal. Prev. 150, 105908 (2021). (PMID: 3331043110.1016/j.aap.2020.105908)
Wang, X., Xu, R., Zhang, S., Zhuang, Y. & Wang, Y. Driver distraction detection based on vehicle dynamics using naturalistic driving data. Transp. Res. Part C Emerg. Technol. 136, 103561 (2022). (PMID: 10.1016/j.trc.2022.103561)
Huemer, A. K., Schumacher, M., Mennecke, M. & Vollrath, M. Systematic review of observational studies on secondary task engagement while driving. Accid. Anal. Prev. 119, 225–236 (2018). (PMID: 3005551110.1016/j.aap.2018.07.017)
Prat, F., Planes, M., Gras, M. E. & Sullman, M. J. M. An observational study of driving distractions on urban roads in Spain. Accid. Anal. Prev. 74, 8–16 (2014). (PMID: 2546393910.1016/j.aap.2014.10.003)
Kidd, D. G. & Chaudhary, N. K. Changes in the sources of distracted driving among Northern Virginia drivers in 2014 and 2018: A comparison of results from two roadside observation surveys. J. Safety Res. 68, 131–138 (2019). (PMID: 3087650410.1016/j.jsr.2018.12.004)
Beanland, V., Fitzharris, M., Young, K. L. & Lenné, M. G. Driver inattention and driver distraction in serious casualty crashes: Data from the Australian national crash in-depth study. Accid. Anal. Prev. 54, 99–107 (2013). (PMID: 2349998110.1016/j.aap.2012.12.043)
Talbot, R., Fagerlind, H. & Morris, A. Exploring inattention and distraction in the safety net accident causation database. Accid. Anal. Prev. 60, 445–455 (2013). (PMID: 2417610610.1016/j.aap.2012.03.031)
Wundersitz, L. Driver distraction and inattention in fatal and injury crashes: Findings from in-depth road crash data. Traffic Inj. Prev. 20, 696–701 (2019). (PMID: 3140835810.1080/15389588.2019.1644627)
Oviedo-Trespalacios, O., Nandavar, S. & Haworth, N. L. How do perceptions of risk and other psychological factors influence the use of in-vehicle information systems (IVIS)?. Transp. Res. Part F Traffic Psychol. Behav. 67, 113–122 (2019). (PMID: 10.1016/j.trf.2019.10.011)
Yao, X. et al. Analysis of psychological influences on navigation use while driving based on extended theory of planned behavior. Transp. Res. Rec. 2673, 480–490 (2019). (PMID: 10.1177/0361198119845666)
Chen, H. W. & Donmez, B. What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement. Accid. Anal. Prev. 91, 166–174 (2016). (PMID: 2699437110.1016/j.aap.2015.08.015)
Parnell, K. J., Stanton, N. A. & Plant, K. L. Exploring the mechanisms of distraction from in-vehicle technology: The development of the PARRC model. Saf. Sci. 87, 25–37 (2016). (PMID: 10.1016/j.ssci.2016.03.014)
Parnell, K. J., Stanton, N. A. & Plant, K. L. What’s the law got to do with it? Legislation regarding in-vehicle technology use and its impact on driver distraction. Accid. Anal. Prev. 100, 1–14 (2017). (PMID: 2808143310.1016/j.aap.2016.12.015)
Parnell, K. J., Stanton, N. A. & Plant, K. L. What technologies do people engage with while driving and why?. Accid. Anal. Prev. 111, 222–237 (2018). (PMID: 2924507910.1016/j.aap.2017.12.004)
Ziakopoulos, A., Theofilatos, A., Papadimitriou, E. & Yannis, G. A meta-analysis of the impacts of operating in-vehicle information systems on road safety. IATSS Res. 43, 185–194 (2019). (PMID: 10.1016/j.iatssr.2019.01.003)
Romer, D., Lee, Y. C., McDonald, C. C. & Winston, F. K. Adolescence, attention allocation, and driving safety. J. Adolesc. Health 54, S6–S15 (2014). (PMID: 24759442399941210.1016/j.jadohealth.2013.10.202)
Lansdown, T. C. Individual differences and propensity to engage with in-vehicle distractions - A self-report survey. Transp. Res. Part F Traffic Psychol. Behav. 15, 1–8 (2012). (PMID: 10.1016/j.trf.2011.09.001)
Klauer, S. G. et al. Distracted driving and risk of road crashes among novice and experienced drivers. N. Engl. J. Med. 370, 54–59 (2014). (PMID: 24382065418315410.1056/NEJMsa1204142)
Wickens, C. D. Multiple resources and mental workload. Hum. Factors 50, 449–455 (2008). (PMID: 1868905210.1518/001872008X288394)
Bamney, A., Pantangi, S. S., Jashami, H. & Savolainen, P. How do the type and duration of distraction affect speed selection and crash risk? An evaluation using naturalistic driving data. Accid. Anal. Prev. 178, 106854 (2022). (PMID: 3625246610.1016/j.aap.2022.106854)
Jin, L., Xian, H., Niu, Q. & Bie, J. Research on safety evaluation model for in-vehicle secondary task driving. Accid. Anal. Prev. 81, 243–250 (2015). (PMID: 2519498710.1016/j.aap.2014.08.013)
Maciej, J. & Vollrath, M. Comparison of manual vs. speech-based interaction with in-vehicle information systems. Accid. Anal. Prev. 41, 924–930 (2009). (PMID: 1966442810.1016/j.aap.2009.05.007)
Garay-Vega, L. et al. Evaluation of different speech and touch interfaces to in-vehicle music retrieval systems. Accid. Anal. Prev. 42, 913–920 (2010). (PMID: 20380920285467610.1016/j.aap.2009.12.022)
Zhong, Q., Guo, G. & Zhi, J. Address inputting while driving: A comparison of four alternative text input methods on in-vehicle navigation displays usability and driver distraction. Traffic Inj Prev. 23, 163–168 (2022). (PMID: 3531933110.1080/15389588.2022.2047958)
Ma, J., Li, J. & Gong, Z. Evaluation of driver distraction from in-vehicle information systems: A simulator study of interaction modes and secondary tasks classes on eight production cars. Int. J. Ind. Ergon. 92, 103380 (2022). (PMID: 10.1016/j.ergon.2022.103380)
Zhang, T. et al. Input modality matters: A comparison of touch, speech, and gesture based in-vehicle interaction. Appl. Ergon. 108, 103958 (2023). (PMID: 3658750310.1016/j.apergo.2022.103958)
Wang, Y. et al. The validity of driving simulation for assessing differences between in-vehicle informational interfaces: A comparison with field testing. Ergonomics 53, 404–420 (2010). (PMID: 2019141510.1080/00140130903464358)
Large, D. R., Pampel, S. M., Merriman, S. E. & Burnett, G. A validation study of a fixed-based, medium fidelity driving simulator for human-machine interfaces visual distraction testing. IET Intell. Transp. Syst. 17, 1104–1117 (2023). (PMID: 10.1049/itr2.12362)
Chiang, D. P., Brooks, A. M. & Weir, D. H. Comparison of visual-manual and voice interaction with contemporary navigation system HMIs. SAE Trans. 114, 436–443 (2005).
Mehler, B. et al. Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems. Ergonomics 59(3), 344–367 (2016). (PMID: 2626928110.1080/00140139.2015.1081412)
Reimer, B. et al. Patterns in transitions of visual attention during baseline driving and during interaction with visual-manual and voice-based interfaces. Ergonomics 64(11), 1429–1451 (2021). (PMID: 3401891610.1080/00140139.2021.1930197)
Cooper, J. M. et al. Age-related differences in the cognitive, visual, and temporal demands of in-vehicle information systems. Front. Psychol. 11, 1154 (2020). (PMID: 32581959728354010.3389/fpsyg.2020.01154)
Harvey, C., Stanton, N. A., Pickering, C. A., McDonald, M. & Zheng, P. A usability evaluation toolkit for in-vehicle information systems (IVISs). Appl. Ergon. 42, 563–574 (2011). (PMID: 2103634710.1016/j.apergo.2010.09.013)
Kim, H., Kwon, S., Heo, J., Lee, H. & Chung, M. K. The effect of touch-key size on the usability of in-vehicle information systems and driving safety during simulated driving. Appl. Ergon. 45, 379–388 (2014). (PMID: 2375979110.1016/j.apergo.2013.05.006)
Kujala, T. Browsing the information highway while driving: Three in-vehicle touch screen scrolling methods and driver distraction. Pers. Ubiquit. Comput. 17, 815–823 (2013). (PMID: 10.1007/s00779-012-0517-2)
Jung, S. et al. Effect of touch button interface on in-vehicle information systems usability. Int. J. Hum. Comput. Interact. 37, 1404–1422 (2021). (PMID: 10.1080/10447318.2021.1886484)
Mitsopoulos-Rubens, E., Trotter, M. J. & Lenné, M. G. Effects on driving performance of interacting with an in-vehicle music player: A comparison of three interface layout concepts for information presentation. Appl. Ergon. 42, 583–591 (2011). (PMID: 2086969410.1016/j.apergo.2010.08.017)
Oviedo-Trespalacios, O., Haque, M. M., King, M. & Washington, S. Self-regulation of driving speed among distracted drivers: An application of driver behavioral adaptation theory. Traffic Inj. Prev. 18, 599–605 (2017). (PMID: 2809502610.1080/15389588.2017.1278628)
Miller, E. E., Boyle, L. N., Jenness, J. W. & Lee, J. D. Voice control tasks on cognitive workload and driving performance: Implications of modality, difficulty, and duration. Transp. Res. Rec. 2672, 84–93 (2018). (PMID: 10.1177/0361198118797483)
Biondi, F. N., Getty, D., Cooper, J. M. & Strayer, D. L. Examining the effect of infotainment auditory-vocal systems’ design components on workload and usability. Transp. Res. Part F Traffic Psychol. Behav. 62, 520–528 (2019). (PMID: 10.1016/j.trf.2019.02.006)
Kim, H. & Gabbard, J. L. Assessing distraction potential of augmented reality head-up displays for vehicle drivers. Hum Factors 64, 852–865 (2022). (PMID: 3106339910.1177/0018720819844845)
Graichen, L., Graichen, M. & Krems, J. F. Effects of gesture-based interaction on driving behavior: A driving simulator study using the projection-based vehicle-in-the-loop. Hum Factors 64, 324–342 (2022). (PMID: 3279520010.1177/0018720820943284)
Jung, T., Kass, C., Zapf, D. & Hecht, H. Effectiveness and user acceptance of infotainment-lockouts: A driving simulator study. Transp. Res. Part F Traffic Psychol. Behav. 60, 643–656 (2019). (PMID: 10.1016/j.trf.2018.12.001)
Onate-Vega, D., Oviedo-Trespalacios, O. & King, M. J. How drivers adapt their behaviour to changes in task complexity: The role of secondary task demands and road environment factors. Transp. Res. Part F Traffic Psychol. Behav. 71, 145–156 (2020). (PMID: 10.1016/j.trf.2020.03.015)
معلومات مُعتمدة: 52175253 National Natural Science Foundation of China; 2022YFB4301202-20 National Key Research and Development Program of China; 2682023ZTPY042 Interdisciplinary Research Project of Southwest Jiaotong University; YG2022006 New Interdisciplinary Cultivation Program of Southwest Jiaotong University
تواريخ الأحداث: Date Created: 20240831 Date Completed: 20240831 Latest Revision: 20240904
رمز التحديث: 20240904
مُعرف محوري في PubMed: PMC11366028
DOI: 10.1038/s41598-024-71226-4
PMID: 39217232
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-024-71226-4