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Title of the article ELECTRONIC SYSTEMS OF INTELLIGENT VEHICLES
Authors

ENDACHEV Denis V., Ph. D. in Eng., Executive Director on Information and Intelligent Systems, State Research Center of the Russian Federation FSUE “NAMI”, Moscow, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.">denis.endachev@nami.r

BAKHMUTOV Sergey V., D. Sc. in Eng., Prof., Deputy CEO for Science, State Research Center of the Russian Federation FSUE “NAMI”, Moscow, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.">This email address is being protected from spambots. You need JavaScript enabled to view it.

EVGRAFOV Vladimir V., Ph. D. in Phys. and Math., Director of the Center of Intelligent Systems, State Research Center of the Russian Federation FSUE “NAMI”, Moscow, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.">vladimir.evgrafov@nami.r

MEZENTCEV Nikolay P., Head of the Departament of Intelligent Vehicles, State Research Center of the Russian Federation FSUE “NAMI”, Moscow, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.">This email address is being protected from spambots. You need JavaScript enabled to view it.

In the section GENERAL ISSUES OF MECHANICS
Year 2020
Issue 4
Pages 5–10
Type of article RAR
Index UDK 656.051
DOI https://doi.org/10.46864/1995-0470-2020-4-53-5-10
Abstract Modern automotive engineering is closely related to the implementation of information systems. In automobile transport, the range of such developments is considerably wide: from driver assistance systems (ADAS — Advanced Driver Assistance System) to full autopilot systems. The article provides a brief overview of the state of the problem and presents the main directions of development of the State Research Center of the Russian Federation FSUE “NAMI” in the field of ADAS and highly automated (unmanned) vehicles. Descriptions of on-board vehicle systems of a high level of automation are given developed by the State Research Center of the Russian Federation FSUE “NAMI” with the participation of manufacturers. The article also describes the key technologies of machine vision systems, test sites for highly automated vehicles.
Keywords driver assistance system, autonomous vehicle, electronic ssystem, active safety
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Bibliography
  1. SAE J3016:201806. Taxonomy and definitions for terms related to driving automation systems for onroad motor vehicles. 2018. Available at: https://www.sae.org/standards/content/j3016_201806/ (accessed 10 November 2020).
  2. Spena R.M., Timpone F., Farroni F. Virtual testing of advanced driving assistance systems. International journal of mechanics, 2015, vol. 9, pp. 300–308. Available at: https://www.researchgate.net/publication/286186075_Virtual_Testing_
    of_Advanced_Driving_Assistance_Systems (accessed 10 November 2020).
  3. Rothfuβ S., Schmidt R., Flad M., Hohmann S. A concept for humanmachine negotiation in advanced driving assistance systems. Proc. International conference on systems, man and cybernetics (SMC). 2019. DOI: 10.1109/SMC.2019.8914282.
  4. Giacalone J.P. From advanced driving assistance systems to autonomous drive: the complexity challenge. Proc. eSAME 2017. Sophia Antipolis, 2017. Available at: https://www.researchgate.net/publication/322821305_FROM_
    ADVANCED_DRIVING_ASSISTANCE_SYSTEMS_TO_
    AUTONOMOUS_DRIVE_THE_COMPLEXITY_CHALLENGE (accessed 10 November 2020).
  5. Ayachi R., Afif M., Said Y., Abdelali A.B. Pedestrian detection for advanced driving assisting system: a transfer learning approach. Proc. 5th International conference on advanced technologies for signal and image processing (ATSIP). Sousse, 2020. Available at: https://www.researchgate.net/publication/344876764_Pedestrian_
    Detection_for_Advanced_Driving_Assisting_System_A_Transfer_Learning_Approach (accessed 10 November 2020).
  6. Khan A.M. Modelling human factors for advanced driving assistance system design. Advances in human aspects of transportation, 2017, pp. 3–14. Available at: https://www.researchgate.net/publication/304996145_Modelling_Human_Factors_fo
    r_Advanced_Driving_Assistance_System_Design (accessed 10 November 2020).
  7. Farag W.A. Traffic signs classification by deep learning for advanced driving assistance systems. Intelligent decision technologies, 2019, vol. 13, no. 3, рp. 305–314.
    DOI: 10.3233/IDT180064.
  8. Farag W.A., Saleh Z. Road lanelines detection in realtime for advanced driving assistance systems. Proc. International conference on innovation and intelligence for informatics, computing, and technologies (3ICT). 2018. DOI: 10.1109/3ICT.2018.8855797.
  9. Yadav R., Dahiya P.K., Mishra R. A high performance 76.5 GHz FMCW RADAR for advanced driving assistance system. Proc. 3rd International conference on signal processing and integrated networks (SPIN). Noida, 2016. DOI: 10.1109/SPIN.2016.7566724.
  10. Matsubayashi Sh., Miwa K., Yamaguchi T., Kamiya T., Suzuki T., Ikeura R., Hayakawa S., Ito T. Cognitive and behavioral effects on driving by information presentation and behavioral intervention in advanced driving assistance system. 2018. Available at: https://www.researchgate.net/publication/342846297_Cognitive_and_Behavioral_
    Effects_on_Driving_by_Information_Presentation_and_Behavioral_Intervention_
    in_Advanced_ Driving_Assistance_System (accessed 10 November 2020).