Concept of transferring control to a driver in highly automated cars
https://doi.org/10.29235/1561-8323-2020-64-5-624-631
Abstract
From an interdisciplinary point of view, on the basis of a systematic approach, the concept of transferring control to a driver in highly automated vehicles was first formulated, which allows one to determine the potential possibility of regaining control of a highly automated car based on its awareness of the situational situation along the route of movement and monitoring of the current functional state, including readiness for emergency actions, and individual features of the driver. The classification of cross-modal information flows in highly automated and autonomous cars is presented. The definition of cross-modal interaction in highly automated vehicles is given, and the goal of analyzing heterogeneous information flows in on-board vehicle systems is formulated, where automatic analysis of heterogeneous information flows gives a synergistic effect. Examples of relevant driver information are provided.
About the Authors
V. V. Savchenko
Joint Institute of Mechanical Engineering of the National Academy of Sciences of Belarus
Belarus
Belarus
Savchenko Vladimir V. - Ph. D. (Engineering), Chief of the Center. Joint Institute of Mechanical Engineering of the National Academy of Sciences of Belarus.
12, Akademicheskaya Str., 220072, Minsk.
S. N. Poddubko
Joint Institute of Mechanical Engineering of the National Academy of Sciences of Belarus
Belarus
Belarus
Poddubko Sergey N. - Ph. D. (Engineering), Associate professor, General director. Joint Institute of Mechanical Engineering of the National Academy of Sciences of Belarus.
12, Akademicheskaya Str., 220072, Minsk.
References
1. Madigan R., Louw T., Merat N. The effect of varying levels of vehicle automation on drivers' lane changing behavior. PLoS ONE, 2018, vol. 13, no. 2, art. e0192190. https://doi.org/10.1371/journal.pone.0192190
2. de Winter J. C. F., Happee R., Martens M. H., Stanton N. A. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: a review of the empirical evidence. VDI Wissensforum. Available at: https://www.vdi-wissensforum.de/news/effects-of-adaptive-cruise-control-and-highly-automated-driving (accessed 17 January 2020).
3. Endsley M. Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics, 1999, vol. 42, no. 3, pp. 462-492. https://doi.org/10.1080/001401399185595
4. Merat N., Jamson A. H., Lai F. C. H., Carsten O. Highly automated driving, secondary task performance and driver state. Human Factors, 2012, vol. 54, no. 5, pp. 762-771. https://doi.org/10.1177/0018720812442087
5. Merat N., Jamson A. H., Lai F. C. H., Daly M., Carsten O. M. J. Transition to manual: driver behavior when resuming control from a highly automated vehicle. Transportation Research Part F: Traffic Psychology and Behavior, 2014, vol. 27, pp. 274-282. https://doi.org/10.1016/j.trf.2014.09.005
6. Savchenko V. V. Optimization of the Parameters of Semantic Biological Feedback in Systems of Monitoring the Functional State of Operators. Journal of Automation and Information Sciences, 2009, vol. 41, no. 1, pp. 75-80. https://doi.org/10.1615/jautomatinfscien.v41.i1.80
7. Dementienko V. V. Driver vigilance remote monitoring system. Science Journal of Transportation, 2015, no. 6, pp. 110-114.
8. Savchenko V. V., Svistun M. S., Sikorski V. V. Driver Support System: Test and Experimental Results. Avtomo-bil'naya promyshlennost' [Automotive industry], 2008, no. 1, pp. 32-34 (in Russian).
9. Dementienko V. V., Dorokhov V. B. Estimation of efficiency of fatigue monitoring devices considering the occurrence of the instability of nature of falling asleep errors. Zhurnal vysshei nervnoi deyatel'nosti imeni I. P. Pavlova [I. P. Pavlov Journal of Higher Nervous Activity], 2013, vol. 63, no. 1, pp. 24-32 (in Russian). https://doi.org/10.7868/S0044467713010036
10. Savchenko V. V. Methods and Facilities for Higher Functioning Efficiency of “Man-Machine” System Operators. Vestsi Natsyyanal'nai akademii navuk Belarusi. Seryya fizika-technichnych navuk = Proceedings of the National Academy of Sciences of Belarus. Physical-technical series, 2005, no. 2, pp. 29-37 (in Russian).
11. Kremez A. S., Bonch-Bruevich V. V. Psychological aspects of the safety of the operating personnel of technological installations. Avtomatizatsiya v promyshlennosti [Automation in Industry], 2011, no. 7, pp. 43-48 (in Russian).
12. Psychological certification of drivers. VIENNA TESTING SYSTEM. Available at: https://docplayer.ru/28720064-Psihologicheskaya-attestaciya-voditeley-sistema-testirovaniya-vienna.html (accessed 29 January 2020).
13. Intel: each robo-car will generate an average of 4 TB of data per day. 3DNews Daily Digital Digest. Available at: https://www.3dnews.ru/951292.html (accessed 29 January 2020).
14. Savchenko V. V., Poddubko S. N. Integration of passive driver assistance systems with on-board vehicle systems. Intellektual'nye transportnye sistemy [Intelligent transport systems]. Moscow, 2017 (in Russian).
15. Savchenko V. V., Poddubko S. N. An approach to development of a method for transfer of vehicle control from on-board systems to the driver in authomtic mode. Trudy NGTU im. R. E. Alekseeva = Transactions of NNSTU n.a. R. E. Alekseev, 2018, no. 2(121), pp. 181-187 (in Russian).
16. Savchenko V. V., Poddubko S. N. Cross-modal information flows in highly automated vehicles. IOP Conference Series: Materials Science and Engineering, 2019, vol. 534, art. 012003. https://doi.org/10.1088/1757-899x/534/1/012003
17. The most detailed maps of the world will be needed by cars, not people. Security-Corp. org: a site about information security, 2017. Available at: http://security-corp.org/programming/vebdev/39026-samye-podrobnye-karty-mira-budut-nuzhny-avtomobilyam-a-ne-lyudyam.html (accessed 29 January 2020).
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