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Analysis of Dynamic Characteristics of Man-Machine Co-Driving Vehicle during Driving Right Switching

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Abstract

In order to study the influence of different drivers and vehicle speeds on the dynamics of smart cars when the driving rights are switched; this paper analyzes them through driving experiments. This experiment recruited 16 participants, and built a virtual experimental platform for man-machine co-driving, and designed an experimental program at three speeds of 50, 80, and 120 km/h based on 8 s early warning time interval for driving rights to take over. And the experimental data is processed and analyzed by K-means clustering. The results show that the driver’s age and driving experience affect the driver’s take-over behavior and the dynamics of the vehicle. The take-over behavior taken by the high driving experience or young driver in the process of driving right switching can ensure that the vehicle has better stability. On the one hand, the increase in vehicle speed will affect the driver’s take-over behavior. On the other hand, it will cause the nonlinear characteristic of the vehicle to be significant and the driving stability of the vehicle to be worse.

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REFERENCES

  1. Young, M.S., Stanton, N.A., and Harris, D., Driving automation: Learning from aviation about design philosophies, Int. J. Veh. Des., 2007, vol. 45, no. 3, pp. 323–338.  https://doi.org/10.1504/IJVD.2007.014908

    Article  Google Scholar 

  2. Google Self-Driving Car Project. https://www.google.com/selfdrivingcar/. Cited April 5, 2016.

  3. Toyota Automated Highway Driving Assist. https://www.toyota-europe.com/world-of-toyota/safetytechnology/toyota-automatic-highway-driving-assist.json. Cited April 5, 2016.

  4. Tsugawa, S., Kato, S., and Aoki, K., An automated truck platoon for energy saving, IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, San Francisco, Calif., 2011, pp. 4109–4114.  https://doi.org/10.1109/IROS.2011.6094549

  5. Viswanath, P., Chitnis, K., Swami, P., Mody, M., Shivalingappa, S., Nagori, S., Mathew, M., Desappan, K., Jagannathan, S., Poddar, D., Jain, A., Garud, H., Appia, V., Mangla, M., and Dabral, S., A diverse low cost high performance platform for advanced driver assistance system (ADAS) applications, IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, Nev., 2016, IEEE, 2016, pp. 819–827.  https://doi.org/10.1109/CVPRW.2016.107

  6. Federal automated vehicles policy—Accelerating the next revolution in roadway safety. U.S. Department of Transportation, Washington: National Highway Traffic Safety Administration, 2016.

  7. Russell, H.E.B., Harbott, L.K., Nisky, I., Pan, S., Okamura, A.M., and Gerdes, J.C., Motor learning affects car-to-driver handover in automated vehicles, Sci. Rob., 2016, vol. 1, no. 1, p. eaah5682.  https://doi.org/10.1126/scirobotics.aah5682

  8. Gold, C., Körber, M., Lechner, D., and Bengler, K., Taking over control from highly automated vehicles in complex traffic situations: The role of traffic density, Hum. Factors: J. Hum. Factors Ergon. Soc., 2016, vol. 58, no. 4, pp. 642–652.  https://doi.org/10.1177/0018720816634226

    Article  Google Scholar 

  9. Heenan, A., Herdman, C.M., Brown, M.S., and Robert, N., Effects of conversation on situation awareness and working memory in simulated driving, Hum. Factors: J. Hum. Factors Ergon. Soc., 2014, vol. 56, no. 6, pp. 1077–1092.  https://doi.org/10.1177/0018720813519265

    Article  Google Scholar 

  10. Wang, Y., Research on driving task based on human-computer interaction simulation, PhD Dissertation, Beijing: Tsinghua Univ., 2009.

  11. Dogan, E., Rahal, M.-C., Deborne, R., Delhomme, P., Kemeny, A., and Perrin, J., Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement, Transp. Res. F: Traffic Psychol. Behav., 2017, vol. 46, part A, pp. 205–215.  https://doi.org/10.1016/j.trf.2017.01.012

  12. Stevens, A. and Paulo, D., The use of mobile phones while driving: A review, Transport Research Laboratory, 1997.

    Google Scholar 

  13. Strayer, D.L., Drews, F.A., and Crouch, D.J., A comparison of the cell phone driver and the drunk driver, Hum. Factors: J. Hum. Factors Ergon. Soc., 2006, vol. 48, no. 2, pp. 381–391.  https://doi.org/10.1518/001872006777724471

    Article  Google Scholar 

  14. Turner, M.L., Fernandez, J.E., and Nelson, K., The effect of music amplitude on the reaction to unexpected visual events, J. Gen. Psychol., 1996, vol. 123, no. 1, pp. 51–62.  https://doi.org/10.1080/00221309.1996.9921259

    Article  Google Scholar 

  15. Louw, T. and Merat, N., Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation, Transp. Res. C: Emerging Technol., 2017, vol. 76, pp. 35–50.  https://doi.org/10.1016/j.trc.2017.01.001

    Article  Google Scholar 

  16. Gu, W.L., Research on the influence of autonomous driving technology on human-machine interface design in car, Ind. Des., 2019, no. 11, pp. 145–146.

  17. Bazilinskyy, P. and de Winter, J., Auditory interfaces in automated driving: An international survey, PeerJ Comput. Sci., 2015, vol. 1, p. e13.  https://doi.org/10.7717/peerj-cs.13

    Article  Google Scholar 

  18. Naujoks, F., Mai, C., and Neukum, A., The effect of urgency of take-over requests during highly automated driving under distraction conditions, Advances in Human Aspects of Transportation: Part I, Stanton, N., Di Bucchianico, G., Vallicelli, A., and Landry, S., Eds., Las Vegas: AHFE International, 2014, pp. 431–439.

    Google Scholar 

  19. Petermeijer, S.M., de Winter, J.C.F., and Bengler, K.J., Vibrotactile displays: A survey with a view on highly automated driving, IEEE Trans. Intell. Transp. Syst., 2016, vol. 17, no. 4, pp. 897–907.  https://doi.org/10.1109/TITS.2015.2494873

    Article  Google Scholar 

  20. Schwalk, M., Kalogerakis, N., and Maier, T., Driver support by a vibrotactile seat matrix-recognition, adequacy and workload of tactile patterns in take-over scenarios during automated driving, Procedia Manuf., 2015, vol. 3, pp. 2466–2473.  https://doi.org/10.1016/j.promfg.2015.07.507

    Article  Google Scholar 

  21. Telpaz, A., Rhindress, B., Zelman, I., and Tsimhoni, O., Haptic seat for automated driving: Preparing the driver to take control effectively, Proc. 7th Int. Conf. on Automotive User Interfaces and Interactive Vehicular Applications, Nottingham, 2015, New York: Association for Computing Machinery, 2015, pp. 23–30.  https://doi.org/10.1145/2799250.2799267

  22. Van Erp, J.B.F., Toet, A., and Janssen, J.B., Uni-, bi- and tri-modal warning signals: Effects of temporal parameters and sensory modality on perceived urgency, Saf. Sci., 2015, vol. 72, pp. 1–8.  https://doi.org/10.1016/j.ssci.2014.07.022

    Article  Google Scholar 

  23. Bazilinskyy, P., Kyriakidis, M., and de Winter, J., An international crowdsourcing study into people’s statements on fully automated driving, Procedia Manuf., 2015, vol. 3, pp. 2534–2542.  https://doi.org/10.1016/j.promfg.2015.07.540

    Article  Google Scholar 

  24. Gray, R., Ho, C., and Spence, C., A comparison of different informative vibrotactile forward collision warnings: Does the warning need to be linked to the collision event? PLoS ONE, 2014, vol. 9, no. 1, p. e87070.  https://doi.org/10.1371/journal.pone.0087070

    Article  Google Scholar 

  25. Nukarinen, T., Rantala, J., Farooq, A., and Raisamo, R., Delivering directional haptic cues through eyeglasses and a seat, IEEE World Haptics Conf., Evanston, Ill., 2015, IEEE, 2015, pp. 345–350.  https://doi.org/10.1109/WHC.2015.7177736

  26. Petermeijer, S., Bazilinskyy, P., Bengler, K., and de Winter, J., Take-over again: Investigating multimodal and directional TORs to get the driver back into the loop, Appl. Ergon., 2017, vol. 62, pp. 204–215.  https://doi.org/10.1016/j.apergo.2017.02.023

    Article  Google Scholar 

  27. Liqiang, J., Qingnian, W., and Chuanxue, S., Simulation of electric vehicle of dynamic control system of four-wheel independent drive, J. Jilin Univ. Eng. Ed., 2004, vol. 34, no. 4, pp. 547–554.

    Google Scholar 

  28. Mok, B., Johns, M., Lee, K.J., Miller, D., Sirkin, D., Ive, P., and Ju, W., Emergency, automation off: Unstructured transition timing for distracted drivers of automated vehicles, IEEE 18th Int. Conf. on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, IEEE, 2015, pp. 2458–2464.  https://doi.org/10.1109/ITSC.2015.396

  29. Jiawei, H., Kamber, M., and Pei, J., Data Mining Concept and Techniques, Amsterdam: Morgan Kaufmann, 2012, 3rd ed.

    MATH  Google Scholar 

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Authors and Affiliations

  1. School of Transportation and Vehicle Engineering, Shandong University of Technology, 255049, Zhangdian, Zibo, China

    Guangcheng Ge

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  1. Guangcheng Ge
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Correspondence to Guangcheng Ge.

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Guangcheng Ge Analysis of Dynamic Characteristics of Man-Machine Co-Driving Vehicle during Driving Right Switching. Aut. Control Comp. Sci. 56, 166–179 (2022). https://doi.org/10.3103/S0146411622020055

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