Article Sidebar

Download
PDF
Statistic
Read Counter : 729 Download : 635

Downloads

Download data is not yet available.

Main Article Content

Abstract

The naturalistic study investigated the potential influence of personal driving preferences (assertive and defensive driving style) on users; comfort when being driven in an automated vehicle with a defensive driving style. Adopted the Wizard of Oz design, the study involved three phases: pre-, during, and post-driven to measure their comfort, perceived safety, and likeness as well as motion sickness propensity through self-report questionnaire and heart rate variation. After answering a set of questionnaires, participants were exposed to simulated driving in an automated vehicle with a defensive driving style. A statistical analysis produced no statistically significant difference between assertive and defensive participants. This indicates an overall preference, perceived comfort without severe motion sickness propensity to the defensive driving style of the autonomous vehicle, regardless of participants’ personal driving styles.

Keywords

Autonomous Vehicle Driving Style Human Factors Comfort

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. D. J. Fagnant and K. Kockelman, “Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations,” Transportation Research Part A: Policy and Practice, vol. 77, pp. 167–181, 2015, doi: 10.1016/j.tra.2015.04.003.
  2. R. Krueger, T. H. Rashidi, and J. M. Rose, “Preferences for shared autonomous vehicles,” Transportation Research Part C: Emerging Technologies, vol. 69, pp. 343–355, 2016, doi: 10.1016/j.trc.2016.06.015.
  3. D. Nicolaides, D. Cebon, and J. Miles, “An autonomous taxi service for sustainable urban transportation,” 2017 Smart Cities Symposium Prague, SCSP 2017 - IEEE Proceedings, 2017, doi: 10.1109/SCSP.2017.7973353.
  4. Bureau of Infrastructure, “Transport and Regional Economics (BITRE), Road trauma Australia, 2014 statistical summary bitre, Canberra ACT,” Canberra, 2015.
  5. J. Arbib and T. Seba, “Rethinking Transportation 2020-2030,” 2017.
  6. AAA Foundation for Traffic Safety, “Aggressive Driving: Research Update,” Washington, DC, 2009.
  7. Z. Htike, G. Papaioannou, E. Siampis, E. Velenis, and S. Longo, “Minimisation of Motion Sickness in Autonomous Vehicles,” IEEE Intelligent Vehicles Symposium, Proceedings, no. January 2021, pp. 1135–1140, 2020, doi: 10.1109/IV47402.2020.9304739.
  8. M. Elbanhawi, M. Simic, and R. Jazar, “In the Passenger Seat: Investigating Ride Comfort Measures in Autonomous Cars,” IEEE Intelligent Transportation Systems Magazine, vol. 7, no. 3, pp. 4–17, 2015, doi: 10.1109/MITS.2015.2405571.
  9. J. Van Brummelen, M. O’Brien, D. Gruyer, and H. Najjaran, “Autonomous vehicle perception: The technology of today and tomorrow,” Transportation Research Part C: Emerging Technologies, vol. 89, no. January, pp. 384–406, 2018, doi: 10.1016/j.trc.2018.02.012.
  10. N. M. Yusof, J. Karjanto, J. Terken, F. Delbressine, M. Z. Hassan, and M. Rauterberg, “The exploration of autonomous vehicle driving styles: Preferred longitudinal, lateral, and vertical accelerations,” AutomotiveUI 2016 - 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Proceedings, pp. 245–252, 2016, doi: 10.1145/3003715.3005455.
  11. M. Michałowska and M. Ogłoziński, “Autonomous vehicles and road safety,” Communications in Computer and Information Science, vol. 715, pp. 191–202, 2017, doi: 10.1007/978-3-319-66251-0_16.
  12. C. Lv, X. Hu, A. Sangiovanni-Vincentelli, Y. Li, C. M. Martinez, and D. Cao, “Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach,” IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 2965–2975, 2018, doi: 10.1109/TIE.2018.2850031.
  13. E. Gilman, A. Keskinarkaus, S. Tamminen, S. Pirttikangas, J. Röning, and J. Riekki, “Personalised assistance for fuel-efficient driving,” Transportation Research Part C: Emerging Technologies, vol. 58, no. PD, pp. 681–705, 2015, doi: 10.1016/j.trc.2015.02.007.
  14. P. Bazilinskyy, T. Sakuma, and J. de Winter, “What driving style makes pedestrians think a passing vehicle is driving automatically?,” Applied Ergonomics, vol. 95, no. February, p. 103428, 2021, doi: 10.1016/j.apergo.2021.103428.
  15. G. M. I. Voß, C. M. Keck, and M. Schwalm, “Investigation of drivers’ thresholds of a subjectively accepted driving performance with a focus on automated driving,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 56, pp. 280–292, 2018, doi: 10.1016/j.trf.2018.04.024.
  16. H. Bellem, T. Schönenberg, J. F. Krems, and M. Schrauf, “Objective metrics of comfort: Developing a driving style for highly automated vehicles,” Transportation Research Part F: Traffic Psychology and Behaviour, 2016, doi: 10.1016/j.trf.2016.05.005.
  17. C. Basu, Q. Yang, D. Hungerman, M. Singhal, and A. D. Dragan, “Do you want your autonomous car to drive like you?,” arXiv, pp. 417–425, 2017, doi: 10.48550/arXiv.1802.01636.
  18. J. Karlsson, S. van Waveren, C. Pek, I. Torre, I. Leite, and J. Tumova, “Encoding Human Driving Styles in Motion Planning for Autonomous Vehicles,” in ICRA International Conference on Robotics and Automation, 2021, no. Icra, pp. 1050–1056, doi: 10.1109/icra48506.2021.9561777.
  19. F. Ekman, M. Johansson, L. O. Bligård, M. A. Karlsson, and H. Strömberg, “Exploring automated vehicle driving styles as a source of trust information,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 65, pp. 268–279, 2019, doi: 10.1016/j.trf.2019.07.026.
  20. S. Hecker, D. Dai, and L. Van Gool, “Learning Accurate, Comfortable and Human-like Driving,” arXiv, 2019, doi: https://doi.org/10.48550/arXiv.2007.07218.
  21. X. Sun et al., “Exploring Personalised Autonomous Vehicles to Influence User Trust,” Cognitive Computation, vol. 12, no. 6, pp. 1170–1186, 2020, doi: 10.1007/s12559-020-09757-x.
  22. K. Mühl, C. Strauch, C. Grabmaier, S. Reithinger, A. Huckauf, and M. Baumann, “Get Ready for Being Chauffeured: Passenger’s Preferences and Trust While Being Driven by Human and Automation,” Human Factors, vol. 62, no. 8, pp. 1322–1338, 2020, doi: 10.1177/0018720819872893.
  23. O. Taubman-Ben-Ari, M. Mikulincer, and O. Gillath, “The multidimensional driving style inventory - Scale construct and validation,” Accident Analysis and Prevention, vol. 36, no. 3, pp. 323–332, 2004, doi: 10.1016/S0001-4575(03)00010-1.
  24. M. Zuckerman, D. Michael Kuhlman, M. Joireman, and H. Kraft, “Five robust questionnaire scale factors of personality without culture,” Personality and Individual Differences, vol. 12, no. 9, pp. 929–941, 1993.
  25. K. Kim and M. Park, “Guiding Preferred Driving Style Using Voice in Autonomous Vehicles : An On-Road Wizard-of-Oz Study,” Association for Computing Machinery, New York, NY, USA, pp. 352–364, 2021, doi: 10.1145/3461778.3462056.
  26. P. Wang, S. Sibi, B. Mok, and W. Ju, “Marionette: Enabling On-Road Wizard-of-Oz Autonomous Driving Studies,” ACM/IEEE International Conference on Human-Robot Interaction, vol. Part F1271, pp. 234–243, 2017, doi: 10.1145/2909824.3020256.
  27. J. Karjanto, N. M. Yusof, J. Terken, F. Delbressine, M. Rauterberg, and M. Z. Hassan, “Development of On-Road Automated Vehicle Simulator for Motion Sickness Studies,” International Journal of Driving Science, vol. 1, no. 1, pp. 1–12, 2018, doi: 10.5334/ijds.8.
  28. J. Karjanto et al., “An On-Road Study in Mitigating Motion Sickness When Reading in Automated Driving,” Journal of Hunan University（Natural Sciences）, vol. 48, no. 3, 2021.
  29. M. Turner and M. J. Griffin, “Motion sickness in public road transport: Passenger behaviour and susceptibility,” Ergonomics, vol. 42, no. 3, pp. 444–461, 1999, doi: 10.1080/001401399185586.
  30. B. E. Donohew and M. J. Griffin, “Motion sickness: Effect of the frequency of lateral oscillation,” Aviation Space and Environmental Medicine, vol. 75, no. 8, pp. 649–656, 2004.
  31. M. J. Griffin and M. M. Newman, “An experimental study of low-frequency motion in cars,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 218, no. 11, pp. 1231–1238, 2004, doi: 10.1243/0954407042580093.
  32. A. Lawther and M. J. Griffin, “Prediction of the incidence of motion sickness from the magnitude, frequency, and duration of vertical oscillation,” Journal of the Acoustical Society of America, vol. 82, no. 3, pp. 957–966, 1987, doi: 10.1121/1.395295.
  33. J. F. Golding, M. A. G., and G. Michael A., “A motion sickness maximum around the 0.2 Hz frequency range of horizontal translational oscillation.,” Aviation, space, and environmental medicine, vol. 72, no. 3, pp. 188–198, 2001.
  34. P. J. Gianaros, E. R. Muth, J. T. Mordkoff, M. E. Levine, and R. M. Stern, “A questionnaire for the assessment of the multiple dimensions of motion sickness,” Aviation Space and Environmental Medicine, vol. 72, no. 2, pp. 115–119, 2001.
  35. L. Alexandros and X. Michalis, “The physiological measurements as a critical indicator in users’ experience evaluation,” in Proceedings of the 17th Panhellenic Conference on Informatics - PCI ’13, 2013, pp. 258–263, doi: 10.1145/2491845.2491883.
  36. A. M. A. Zaidi, M. J. Ahmed, and A. S. M. Bakibillah, “Feature extraction and characterization of cardiovascular arrhythmia and normal sinus rhythm from ECG signals using LabVIEW,” 2017 IEEE International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2017, no. November, 2017, doi: 10.1109/ICIVPR.2017.7890871.
  37. J. A. Molino, K. S. Opiela, B. J. Katz, and M. J. Moyer, “Validate First; Simulate Later: A New Approach Used at the FHWA Highway Driving Simulator,” Proceedings of Driver Simulation Conference, North America, Orando, FL, pp. 411–420, 2005.
  38. J. Karjanto, N. M. Yusof, J. Terken, F. Delbressine, M. Z. Hassan, and M. Rauterberg, “Simulating autonomous driving styles: Accelerations for three road profiles,” MATEC Web of Conferences, vol. 90, pp. 1–16, 2017, doi: 10.1051/matecconf/20179001005.
  39. ANALOG, “Analog Device AD8232.” 2012.
  40. A. S. Prasad and N. Kavanashree, “ECG Monitoring System Using AD8232 Sensor,” Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019, no. Icces, pp. 976–980, 2019, doi: 10.1109/ICCES45898.2019.9002540.
  41. A. Rahman, T. Rahman, N. H. Ghani, S. Hossain, and J. Uddin, “IoT Based patient monitoring system using ECG sensor,” 1st International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2019, pp. 378–382, 2019, doi: 10.1109/ICREST.2019.8644065.
  42. Broadcom, “Broadcom Optocoupler PS2506-1-A.,” 2017. .
  43. S. Laborde, E. Mosley, and J. F. Thayer, “Heart rate variability and cardiac vagal tone in psychophysiological research - Recommendations for experiment planning, data analysis, and data reporting,” Frontiers in Psychology, vol. 8, no. FEB, pp. 1–18, 2017, doi: 10.3389/fpsyg.2017.00213.
  44. J. Karjanto, N. Md. Yusof, J. Terken, F. Delbressine, M. Z. Hassan, and M. Rauterberg, “Simulating autonomous driving styles: Accelerations for three road profiles,” MATEC Web of Conferences, vol. 90, p. 1005, 2017, doi: 10.1051/matecconf/20179001005.
  45. T. A. Louis, P. W. Lavori, J. C. Bailar, and M. Polansky, “Crossover And Self-Controlled Design In Clinical Research, New England Journal Of Medicine,” vol. 310, no. 1, pp. 24–31, 1984.
  46. J. Cohen, “Statistical power analysis for the social sciences,” 1988.
  47. Z. Ma and Y. Zhang, “Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors.,” In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 64, no. 1, pp. 2001–2005, 2020.
  48. P. Rossner and A. C. Bullinger, “How do you want to be driven? investigation of different highly-automated driving styles on a highway scenario,” in International Conference on Applied Human Factors and Ergonomics, 2019, pp. 36–43.
  49. F. Pyre, “Analysis of physiological responses induced by motion sickness and its detection based on ocular parameters,” 2021.
  50. T. A. Stoffregen and L. J. Smart, “Postural instability precedes motion sickness,” Brain Research Bulletin, vol. 47, no. 5, pp. 437–448, 1998, doi: 10.1016/S0361-9230(98)00102-6.
  51. E. Faugloire, C. T. Bonnet, M. A. Riley, B. G. Bardy, and T. A. Stoffregen, “Motion sickness, body movement, and claustrophobia during passive restraint,” Experimental brain research, vol. 177, no. 4, pp. 520–532, 2007.
  52. T. A. Stoffregen, L. J. Hettinger, M. W. Haas, M. M. Roe, and L. J. Smart, “Postural instability and motion sickness in a fixed-base flight simulator,” Human Factors, vol. 42, no. 3, pp. 458–469, 2000, doi: 10.1518/001872000779698097.
  53. S. A. Saruchi et al., “applied sciences Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle,” Applied Sciences (Switzerland), 2020.
  54. H. Suzuki, H. Shiroto, and K. Tezuka, “Effects of low frequency vibration on train motion sickness,” Quarterly Report of RTRI (Railway Technical Research Institute) (Japan), vol. 46, no. 1, pp. 35–39, 2005, doi: 10.2219/rtriqr.46.35.
  55. L. LaCount et al., “Static and dynamic autonomic response with increasing nausea perception.,” Aviation, space, and environmental medicine, vol. 82, no. 4, pp. 424–433, 2011, doi: 10.3357/asem.2932.2011.
  56. H. Chu, M.-H. Li, S.-H. Juan, and W.-Y. Chiou, “Effects of transcutaneous electrical nerve stimulation on motion sickness induced by rotary chair: a crossover study,” The Journal of Alternative and Complementary Medicine, vol. 18, no. 5, pp. 494–500, 2012.
  57. F. M. Sulzman, “Life sciences space missions. Overview,” Journal of Applied Physiology, vol. 81, no. 1, pp. 3–6, 1996.
  58. C. S. Stout, W. B. Toscano, and P. S. Cowings, “Reliability of psychophysiological responses across multiple motion sickness stimulation tests.,” Journal of Vestibular Research : Equilibrium & Orientation, vol. 5, no. 1, pp. 25–33, 1995.
  59. L. T. LaCount et al., “Dynamic cardiovagal response to motion sickness: A point-process heart rate variability study,” Computers in Cardiology, vol. 36, pp. 49–52, 2009.

Most read articles by the same author(s)