ABSTRACT
We conducted a daytime naturalistic driving study that involved the same 19 km town itinerary under similar light traffic and fair-weather conditions. We applied a real-time unobtrusive design that could serve as template in future driving studies. In this design, driving parameters and drivers’ arousal levels were captured via a vehicle data acquisition and thermal imaging system, respectively. Analyzing the data, we found that about half of the n = 11 healthy participants exhibited significantly larger arousal reactions to acceleration with respect to the rest of the sample. Acceleration events were of the mundane type, such as entering a highway from an entrance ramp or starting from a red light. The results suggest an underlying grouping of normal drivers with respect to the loading induced by commonplace acceleration. The finding carries implications for certain professions and the design of semi-autonomous vehicles.
- Fatema Akbar, Ayse Elvan Bayraktaroglu, Pradeep Buddharaju, Dennis Rodrigo Da Cunha Silva, Ge Gao, Ted Grover, Ricardo Gutierrez-Osuna, Nathan Cooper Jones, Gloria Mark, Ioannis Pavlidis, 2019. Email makes you sweat: Examining email interruptions and stress using thermal imaging. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–14.Google ScholarDigital Library
- Rahmi Akçelik and DC Biggs. 1987. Acceleration profile models for vehicles in road traffic. Transportation Science 21, 1 (1987), 36–54.Google ScholarDigital Library
- Georg W Alpers, Frank H Wilhelm, and Walton T Roth. 2005. Psychophysiological assessment during exposure in driving phobic patients. Journal of Abnormal Psychology 114, 1 (2005), 126.Google ScholarCross Ref
- Frank B. Baker and Lawrence J. Hubert. 1975. Measuring the Power of Hierarchical Cluster Analysis. J. Amer. Statist. Assoc. 70, 349 (1975), 31–38. https://www.tandfonline.com/doi/abs/10.1080/01621459.1975.10480256Google ScholarCross Ref
- Wolfram Boucsein. 2012. Electrodermal Activity. Springer Science & Business Media, New York, NY, USA.Google Scholar
- Marilynn B Brewer and William D Crano. 2000. Research design and issues of validity. In Handbook of Research Methods in Social and Personality Psychology, Harry T. Reis and Charles M. Judd (Eds.). Cambridge University Press, Cambridge, UK, 3–16.Google Scholar
- Serge Debernard, C Chauvin, R Pokam, and Sabine Langlois. 2016. Designing human-machine interface for autonomous vehicles. IFAC-PapersOnLine 49, 19 (2016), 609–614.Google ScholarCross Ref
- D. Defays. 1977. An efficient algorithm for a complete link method. Comput. J. 20, 4 (April 1977), 364–366. https://doi.org/10.1093/comjnl/20.4.364Google ScholarCross Ref
- Nicole Dillen, Marko Ilievski, Edith Law, Lennart E Nacke, Krzysztof Czarnecki, and Oliver Schneider. 2020. Keep calm and ride along: Passenger comfort and anxiety as physiological responses to autonomous driving styles. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–13.Google ScholarDigital Library
- Laura Eboli, Gabriella Mazzulla, and Giuseppe Pungillo. 2016. Combining speed and acceleration to define car users’ safe or unsafe driving behaviour. Transportation Research Part C: Emerging Technologies 68 (2016), 113–125.Google ScholarCross Ref
- Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in Psychology. Vol. 52. North Holland, Amsterdam, Holland, 139–183.Google Scholar
- Tung Huynh and Ioannis Pavlidis. 2021. Accelarousal Study Dataset - NAT 1. Open Science Frameworkhttps://doi.org/10.17605/OSF.IO/974VF.Google ScholarCross Ref
- Liberty Mutual Insurance. 2020. Welcome to RightTrack by Liberty Mutual. https://www.libertymutual.com/righttrack. Accessed: 2020-08-16.Google Scholar
- J3016_201806 2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. Standard. SAE International, Warrendale, PA.Google Scholar
- Gary Long. 2000. Acceleration characteristics of starting vehicles. Transportation Research Record 1737, 1 (2000), 58–70.Google ScholarCross Ref
- Ismail Bin Mohamad and Dauda Usman. 2013. Standardization and Its Effects on K-Means Clustering Algorithm. Research Journal of Applied Sciences, Engineering and Technology 6, 17 (September 2013), 3299–3303. https://doi.org/10.19026/rjaset.6.3638Google ScholarCross Ref
- Daryl B O’Connor, Mark Conner, Fiona Jones, Brian McMillan, and Eamonn Ferguson. 2009. Exploring the benefits of conscientiousness: An investigation of the role of daily stressors and health behaviors. Annals of Behavioral Medicine 37, 2 (2009), 184–196.Google ScholarCross Ref
- George Panagopoulos and Ioannis Pavlidis. 2020. Forecasting markers of habitual driving behaviors associated With crash risk. IEEE Transactions on Intelligent Transportation Systems 21, 2(2020), 841–851.Google ScholarCross Ref
- I. Pavlidis, M. Dcosta, S. Taamneh, M. Manser, T. Ferris, R. Wunderlich, E. Akleman, and P. Tsiamyrtzis. 2016. Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific Reports 6 (May 2016), 12 pages. https://doi.org/10.1038/srep25651Google ScholarCross Ref
- I. Pavlidis, A. Khatri, P. Buddharaju, M. Manser, R. Wunderlich, E. Akleman, and P. Tsiamyrtzis. 2018. Biofeedback arrests sympathetic and behavioral effects in distracted driving. IEEE Transactions on Affective Computing(2018), 13 pages.Google Scholar
- Peter J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20 (November 1987), 53–65. https://doi.org/10.1016/0377-0427(87)90125-7Google ScholarDigital Library
- Dvijesh Shastri, Manos Papadakis, Panagiotis Tsiamyrtzis, Barbara Bass, and Ioannis Pavlidis. 2012. Perinasal imaging of physiological stress and its affective potential. IEEE Transactions on Affective Computing 3, 3 (2012), 366–378.Google ScholarDigital Library
- Dvijesh Shastri, Ioannis Pavlidis, and Avinash Wesley. 2009. A method to monitor operator overloading. In International Conference on Human-Computer Interaction. Springer, New York, NY, USA, 169–175.Google ScholarDigital Library
- H. Sorensen, D. Jones, M. Heideman, and C. Burrus. 1987. Real-valued fast Fourier transform algorithms. IEEE Transactions on Acoustics, Speech, and Signal Processing 35, 6(1987), 849–863.Google ScholarCross Ref
- Salah Taamneh, Panagiotis Tsiamyrtzis, Malcolm Dcosta, Pradeep Buddharaju, Ashik Khatri, Michael Manser, Thomas Ferris, Robert Wunderlich, and Ioannis Pavlidis. 2017. A multimodal dataset for various forms of distracted driving. Scientific Data 4(2017), 170110.Google ScholarCross Ref
- Panagiotis Tsiamyrtzis, Malcolm Dcosta, Dvijesh Shastri, Eswar Prasad, and Ioannis Pavlidis. 2016. Delineating the operational envelope of mobile and conventional EDA sensing on key body locations. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 5665–5674. https://doi.org/10.1145/2858036.2858536Google ScholarDigital Library
- Dajun Wang, Xin Pei, Li Li, and Danya Yao. 2017. Risky driver recognition based on vehicle speed time series. IEEE Transactions on Human-Machine Systems 48, 1 (2017), 63–71.Google ScholarCross Ref
- Yan Zhou, P. Tsiamyrtzis, P. Lindner, I. Timofeyev, and I. Pavlidis. 2013. Spatiotemporal Smoothing as a Basis for Facial Tissue Tracking in Thermal Imaging. IEEE Transactions on Biomedical Engineering 60, 5 (May 2013), 1280–1289. https://doi.org/10.1109/tbme.2012.2232927Google ScholarCross Ref
Index Terms
- Arousal Responses to Regular Acceleration Events Divide Drivers Into High and Low Groups: A Naturalistic Pilot Study of Accelarousal and Its Implications to Human-Centered Design
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