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Explore User Behaviour in Semi-autonomous Driving

Part of: User-Centred Design

Published online by Cambridge University Press:  26 July 2019

Yuan Shi ,
Jeyhoon Maskani ,
Giandomenico Caruso  and
Monica Bordegoni

Abstract

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The control shifting between a human driver and a semi-autonomous vehicle is one of the most critical scenarios in the road-map of autonomous vehicle development. This paper proposes a methodology to study driver's behaviour in semi-autonomous driving with physiological-sensors-integrated driving simulators. A virtual scenario simulating take-over tasks has been implemented. The behavioural profile of the driver has been defined analysing key metrics collected by the simulator namely lateral position, steering wheel angle, throttle time, brake time, speed, and the take-over time. In addition, heart rate and skin conductance changes have been considered as physiological indicators to assess cognitive workload and reactivity. The methodology has been applied in an experimental study which results are crucial for taking insights on users’ behaviour. Results show that individual different driving styles and performance are able to be distinguished by calculating and elaborating the data collected by the system. This research provides potential directions for establishing a method to characterize a driver's behaviour in a semi-autonomous vehicle.

Keywords

Virtual realitySimulationEvaluationSemi-autonomous vehicleUser behaviour
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 3871 - 3880
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Åhsberg, E., Garnberale, F. and Kjellberg, A. (1997), “Perceived quality of fatigue during different occupational tasks development of a questionnaire”, International Journal of Industrial Ergonomics, Vol. 20 No. 2, pp. 121–135. https://doi.org/10.1016/S0169-8141(96)00044-3Google Scholar
Ariansyah, D., Caruso, G., Ruscio, D. and Bordegoni, M. (2018), “Analysis of autonomic indexes on driver's workload to assess the effect of visual ADAS on user experience and driving performance in different driving conditions”, Journal of Computing and Information Science in Engineering, Vol. 18 No. 3, http://doi.org/10.1115/1.4039313Google Scholar
Banks, V.A. and Stanton, N.A. (2017), “Analysis of driver roles: Modelling the changing role of the driver in automated driving systems using EAST”, Theoretical Issues in Ergonomics Science, https://doi.org/10.1080/1464922X.2017.1395465Google Scholar
Banks, V.A., Eriksson, A., O'Donoghue, J. and Stanton, N.A. (2018), “Is partially automated driving a bad idea? Observations from an on-road study”, Applied ergonomics, Vol. 68, pp. 138–145. https://doi.org/10.1016/j.apergo.2017.11.010Google Scholar
Blanco, M., Atwood, J., Vasquez, H.M., Trimble, T.E., Fitchett, V.L., Radlbeck, J., … and Morgan, J.F. (2015), “Human Factors Evaluation of Level 2 and Level 3 Automated Driving Concepts”, Proceedings of the Transportation Research Board Annual Meeting, Vol. 93, https://doi.org/10.13140/RG.2.1.1874.7361, 2014.07, 320.Google Scholar
Brookhuis, K.A. and de Waard, D. (2010), “Monitoring drivers’ mental workloadin driving simulators using physiological measures”, Accident Analysis and Prevention, Vol. 42 No. 3, pp. 898–903. https://doi.org/10.1016/j.aap.2009.06.001Google Scholar
Reimer, B. and Mehler, B. (2011), “The impact of cognitive workload onphysiological arousal in young adult drivers: a field study and simulation validation”, Ergonomics, Vol. 54 No. 10, pp. 932–942. https://doi.org/10.1080/00140139.2011.604431Google Scholar
Casner, S.M., Hutchins, E.L. and Norman, D. (2016), “The challenges of partially automated driving”, Communications of the ACM, Vol. 59 No. 5, pp. 70–77. https://doi.org/10.1145/2830565Google Scholar
Drosdol, J. and Panik, F. (1985), “The Daimler-Benz Driving Simulator A Tool for Vehicle Development”, SAE Transactions, Vol. 94, pp. 981–997. http://www.jstor.org/stable/44467637.Google Scholar
De Winter, J., Van Leuween, P. and Happee, P. (2012), “Advantages and disadvantages of driving simulators: A discussion”, In: Spink, A. J., Grieco, F., Krips, O. E., Loijens, L. W. S., Noldus, L. P. J. J. and Zimmermann, P. H. (Ed.), Proceedings of the 8th international conference on methods and techniques in behavioral research, Noldus Information Technology bv, Utrecht, The Netherlands, pp. 47–50.Google Scholar
Eriksson, A. and Stanton, N.A. (2017), “Driving performance after self-regulated control transitions in highly automated vehicles”, Human factors, Vol. 59 No. 8, pp. 1233–1248. https://doi.org/10.1177/0018720817728774Google Scholar
Gertman, D.I. and Blackman, H.S. (1994), Human reliability and safety analysis data handbook, John Wiley and Sons.Google Scholar
Heikoop, D.D., de Winter, J.C., van Arem, B. and Stanton, N.A. (2018), “Acclimatizing to automation: driver workload and stress during partially utomated car following in real traffic”. Manuscript submitted.Google Scholar
Louw, T. and Merat, N. (2017), “Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation”, Transportation Research Part C: Emerging Technologies, Vol. 76, pp. 35–50. https://doi.org/10.1016/j.trc.2017.01.001Google Scholar
Logitech. G920 driving force racing wheel and pedals. https://www.logitechg.com/en-roeu/products/driving/driving-force-racing-wheel.html.Google Scholar
Merat, N., Jamson, A.H., Lai, F.C., Daly, M. and Carsten, O.M. (2014), “Transition to manual: Driver behaviour when resuming control from a highly automated vehicle”, Transportation research part F: traffic psychology and behaviour, Vol. 27, pp. 274–282. https://doi.org/10.1016/j.trf.2014.09.005Google Scholar
Merat, N., Seppelt, B., Louw, T., et al. (2018), “The “out-of-the-loop” concept in automated driving: Proposed definition, measures and implications”, Cognition, Technology and Work, pp. 1–12. https://doi.org/10.1007/s10111-018-0525-8Google Scholar
Mercedes-Benz (2013), Distronic Plus with Steering Assist. Available at https://www.mercedes-benz.com. [Accessed 10.12.2018].Google Scholar
Naujoks, F., Purucker, C. and Neukum, A. (2016), “Secondary task engagement and vehicle automation–Comparing the effects of different automation levels in an on-road experiment”, Transportation research part F: traffic psychology and behaviour, Vol. 38, pp. 67–82. https://doi.org/10.1016/j.trf.2016.01.011Google Scholar
Ruscio, D., Bascetta, L., Gabrielli, A., et al. (2017), “Collection and comparison of driver/passenger physiologic and behavioural data in simulation and on-road driving”, Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2017 5th IEEE International Conference on, IEEE, pp. 403–408. https://doi.org/10.1109/MTITS.2017.8005705.Google Scholar
SAE J3016 (2016), Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, J3016-201609: SAE International.Google Scholar
SAE J3114 (2016), Human Factors Definitions for Automated Driving and Related Research Topics, J3114-201612: SAE International.Google Scholar
Saffarian, M., de Winter, J.C. and Happee, R. (2012, September), “Automated driving: human-factors issues and design solutions”, Proceedings of the human factors and ergonomics society annual meeting, Vol. 56 No. 1, pp. 2296–2300. Sage, Los Angeles. https://doi.org/10.1177/1071181312561483.Google Scholar
Shen, S. and Neyens, D.M. (2017), “Assessing drivers’ response during automated driver support system failures with non-driving tasks”, Journal of safety research, Vol. 61, pp. 149–155. https://doi.org/10.1016/j.jsr.2017.02.009Google Scholar
Strand, N., Nilsson, J., Karlsson, I.M. and Nilsson, L. (2014), “Semi-automated versus highly automated driving in critical situations caused by automation failures”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 27, pp. 218–228. https://doi.org/10.1016/j.trf.2014.04.005Google Scholar
Strayer, D.L. and Drew, F.A. (2004), “Profiles in driver distraction: Effects of cell phone conversations on younger and older drivers”, Human factors, Vol. 46 No. 4, pp. 640–649. https://doi.org/10.1518/hfes.46.4.640.56806Google Scholar
Taheri, S.M., Matsushita, K. and Sasaki, M. (2017), “Development of a Driving Simulator with Analyzing Driver's Characteristics Based on a Virtual Reality Head Mounted Display”, Journal of Transportation Technologies, Vol. 7 No. 3, p. 351. http://doi.org/10.4236/jtts.2017.73023Google Scholar
Tesla Motors (2016), Model S Software Version 7.0, https://www.tesla.com/en_GB/presskit/autopilot.Google Scholar
Thought technology. Physiological sensors, http://thoughttechnology.com/.Google Scholar
Unity 3D. https://unity.com/.Google Scholar
Yao, L., Liu, Y., Li, W., Zhou, L., Ge, Y., Chai, J. and Sun, X. (2014, June), “Using physiological measures to evaluate user experience of mobile applications”, International Conference on Engineering Psychology and Cognitive Ergonomics, Cham, Springer, pp. 301–310.Google Scholar