Abstract
Driving is a highly complex task, comprising over 1600 separate tasks over five behavioural levels. 94% of road accidents occur by human faults. Drivers simultaneously control the vehicle, adjust speed and trajectory, deal with hazards, evaluate progress towards their goal, and make strategic decisions such as navigation. Novel technologies such as active cruise control and active steering are intended as comfort systems for the driver because they are designed to relieve the driver of workload. One could go further and imagine whether cars could be created so as to get to know their user and use that knowledge to recognise the user and be more safe. A cooperative driving leads to automotive systems with impacts on mental workload. Of particular concern in this study are the twofold areas. On the one hand the personalization of the car to the user and on the other, its customization. Using the principles of cognitive ergonomics, the main purpose is to allow more features to people, whilst providing comfort and trust in the human machine interface which (1) diagnoses the driver’s state and uses additional monitoring devices (2) to provide feedback about driver´s behaviour as well as awareness and wellness.
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WHO: Work Health Organization (2013). http://www.who.int/en/
Young, M.S., Birrell, S.A., Stanton, N.A.: Safe driving in a green world: a review of driver performance benchmarks and technologies to support ‘smart’ driving. Appl. Ergon. 42(4), 533–539 (2011)
Walker, G.H., Stanton, N.A., Young, M.S.: Hierarchical task analysis of driving: a new research tool. In: Hanson, M.A. (ed.) Contemporary Ergonomics 2001, Proceedings of the Annual Conference of the Ergonomics Society, Cirencester, April 2001, pp. 435–440. Taylor and Francis, London (2001)
Costa, S., Simões, P., Costa, N., Arezes, P.: A cooperative human-machine interaction warning strategy for the semi-autonomous driving context, pp. 1–7, November 2001
Fridman, L., Toyoda, H., Seaman, S., Seppelt, B., Angell, L., Lee, J., Reimer, B.: What can be predicted from six seconds of driver glances? pp. 2805–2813 (2016)
Colombo, M.: Andy Clark, surfing uncertainty: prediction, action, and the embodied mind. Mind. Mach. 27(2), 381–385 (2017)
Engström, J., Bärgman, J., Nilsson, D., Seppelt, B., Markkula, G., Piccinini, G.B., Victor, T.: Great expectations: a predictive processing account of automobile driving. Theoret. Issues Ergon. Sci. 19(2), 156–194 (2018)
Coughlin, J.F., Reimer, B., Mehler, B.: Driver wellness, safety & the development of an awarecar. Mass Inst. Technol. 1–15 (2009)
Colquhoun, H.L., Levac, D., O’Brien, K.K., Straus, S., Tricco, A.C., Perrier, L., Kastner, M., Moher, D.: Scoping reviews: time for clarity in definition, methods, and reporting. J. Clin. Epidemiol. 67(12), 1291–1294 (2014)
Hart, S.G.: NASA-task load index (NASA-TLX); 20 years later. In: Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, pp. 904–908 (2006)
Young, M.S., Brookhuis, K.A., Wickens, C.D., Hancock, P.A.: State of science: mental workload in ergonomics. Ergonomics 58(1), 1–17 (2015). Taylor & Francis
Heine, T., Lenis, G., Reichensperger, P., Beran, T., Doessel, O., Deml, B.: Electrocardiographic features for the measurement of drivers’ mental workload. Appl. Ergon. 61, 31–43 (2017). https://doi.org/10.1016/j.apergo.2016.12.015
Broadbent, D.E.: Perception and Communication. Pergamon Press, Oxford (1958)
Kahneman, D.: Attention and Effort. Prentice-Hall, Englewood Cliffs (1973)
Eggemeier, F.T., Wilson, G.F., Kramer, A.F., Damos, D.L.: Workload assessment in multi-task environments. In: Damos, D.L. (ed.) Multiple Task Performance, pp. 207–216. Taylor & Francis, London (1991)
O’Donnell, R.D., Eggemeier, F.T.: Workload assessment methodology (1986)
Hancock, P.A., Desmond, P.A.: Preface. In: Hancock, P.A., Desmond, P.A. (eds.) Stress, Workload, and Fatigue, pp. 13–15. Lawrence Erlbaum Associates, Mahwah (2001)
Bellet, T., Tattegrain-Veste, H., Chapon, A., Bruyas, M.P., Pachiaudi, G., Deleurence, P., Guilhon, V.: Ingénierie cognitive dans le contexte de l’assistance à la conduite automobile. In: Boy, G. (ed.) Ingénierie cognitive. Lavoisier, Paris (2003)
SAE: Surface Vehicle Recommended Practice – J3016, SAE International, USA (2016)
Meyer, G., Deix, S.: Research and innovation for automated driving in Germany and Europe. In: Road Vehicle Automation, pp. 71–81. Springer International Publishing, Cham (2014)
Okumura, Y.: Activities, findings and perspectives in the field of road vehicle automation in Japan. In: Road Vehicle Automation, pp. 37–46. Springer International Publishing, Cham (2014)
Shaheen, S., Cohen, A.: Innovative Mobility Carsharing Outlook. University of California, Berkeley (2013)
Kyriakidis, M., de Winter, J.C.F., Stanton, N., Bellet, T., van Arem, B., Brookhuis, K., Martens, M.H., Bengler, K., Andersson, J., Merat, N., Reed, N., Flament, M., Hagenzieker, M., Happee, R.: A human factors perspective on automated driving. Theor. Issues Ergon. Sci. (2017). https://doi.org/10.1080/1463922X.2017.1293187
Jamson, A.H., Merat, N., Carsten, O.M.J., Lai, F.C.H.: Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transp. Res. Part C Emerg. Technol. 30, 116–125 (2013)
Ho, D.C., Spence, P.C.: The Multisensory Driver: Implications for Ergonomic Car Interface Design. Ashgate Publishing Ltd., Farnham (2012)
Merat, N., Lee, J.D.: Preface to the special section on suman factors and automation in vehicles: designing highly automated vehicles with the driver in mind. Hum. Factors J. Hum. Factors Ergon. Soc. 54(5), 681–686 (2012). https://doi.org/10.1177/0018720812461374
Meng, F., Gray, R., Ho, C., Ahtamad, M., Spence, C.: Dynamic vibrotactile signals for forward collision avoidance warning systems. Hum. Factors J. Hum. Factors Ergon. Soc. (2014). https://doi.org/10.1177/0018720814542651
Biondi, F., Rossi, R., Gastaldi, M., Mulatti, C.: Beeping ADAS: reflexive effect on drivers’ behavior. Transp. Res. Part F Traffic Psychol. Behav. 25, 27–33 (2014). https://doi.org/10.1016/j.trf.2014.04.020
Eby, D.W., 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, A LongROAD Study (2015)
Pereira, M.S.O.: In-vehicle information systems – related multiple task performance and driver behavior: comparison between different age groups. Doctoral Thesis, Universidade Técnica de Lisboa, Faculdade de Motricidade Humana, Lisboa (2009)
Ryu, J., Chun, J., Park, G., Han, S.H.: Vibro-tactile feedback for information delivery in the vehicle. IEEE Trans. Haptics 3(2), 138–149 (2010). https://doi.org/10.1109/toh.2010.1
Eskandarian, A.: Fundamentals of driver assistance. In: Handbook of Intelligent Vehicles, pp. 491–535). Springer, London (2012)
De Waard, D.: The Measurement of Drivers’ Mental Workload. Groningen University, Traffic Research Center, Netherlands (1996)
Tivesten, E., Dozza, M.: Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving. Transp. Res. Part F: Traffic Psychol. Behav. 26, 258–272 (2014)
He, J., Becic, E., Lee, Y.C., McCarley, J.S.: Mind wandering behind the wheel performance and oculomotor correlates. Hum. Factors: J. Hum. Factors Ergon. Soc. 53(1), 13–21 (2011)
Ratwani, R.M., McCurry, J.M., Trafton, J.G.: Single operator, multiple robots: an eye movement based theoretic model of operator situation awareness. In: Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction, pp. 235–242. IEEE Press (2010)
Wang, Y., Reimer, B., Dobres, J., Mehler, B.: The sensitivity of different methodologies for characterizing drivers’ gaze concentration under increased cognitive demand. Transp. Res. Part F: Traffic Psychol. Behav. 26, 227–237 (2014)
Di Nocera, F., Camilli, M., Terenzi, M.: Using the distribution of eye fixations to assess pilots’ mental workload. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, no. 1, pp. 63–65. Sage Publications, Los Angeles, October 2006
Son, J., Lee, Y., Kim, M.H.: Impact of traffic environment and cognitive workload on older drivers’ behavior in simulated driving. Int. J. Precis. Eng. Manuf. 12(1), 135–141 (2011)
Planing, P.: Innovation Acceptance: The Case of Advanced Driver-Assistance Systems. Springer Science & Business Media, Heidelberg (2014)
Kompass, K., Huber, W., Helmer, T.: Safety and comfort systems: introduction and overview. In: Handbook of Intelligent Vehicles, pp. 605–612. Springer, London (2012)
Bishop, R.: Intelligent vehicle technology and trends (2005)
Dix, A., Finlay, J., Abowd, G., Beale, R.: Universal design. Human-computer interaction, pp. 365–394. Springer, US (2004)
Milakis, D., Snelder, M., van Arem, B., van Wee, B., de Almeida Correia, G.H.: Development and transport implications of automated vehicles in the Netherlands: scenarios for 2030 and 2050. EJTIR 17(1), 63–85 (2017)
Gold, C., Körber, M., Hohenberger, C., Lechner, D., Bengler, K.: Trust in automation – before and after the experience of take-over scenarios in a highly automated vehicle. Procedia Manuf. 3, 3025–3032 (2015)
Mouloua, M., Al-Awar Smither, J., Vincenzi, D.A., Smith, L.: Automation and aging: issues and considerations. In: Advances in Human Performance and Cognitive Engineering Research, pp. 213–237. Emerald Group Publishing Limited, Bingley (2002)
Acknowledgments
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013 and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) Project nº 002797; Funding Reference: POCI-01-0247-FEDER-002797.
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Pereira, E., Costa, S., Costa, N., Arezes, P. (2019). Wellness in Cognitive Workload - A Conceptual Framework. In: Ayaz, H., Mazur, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 775. Springer, Cham. https://doi.org/10.1007/978-3-319-94866-9_36
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DOI: https://doi.org/10.1007/978-3-319-94866-9_36
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