Augmented reality in an intelligent vehicle control system

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Abstract

The use of augmented reality in intelligent vehicle control systems is an important and urgent task for the production and operation of vehicles. Along with the development of sensors, it is necessary to create algorithms and software for such systems. The paper describes a program that simulates the formation of an augmented reality image on a projection display located on the windshield of a car. Augmented reality content modeling is proposed to be carried out by combining the image seen through the windshield and the data coming from the sensors of the intelligent car control system. The functioning of an intelligent vehicle control system is based on the principle of Sensor Fusion, according to which the input data from several discrete sensors are combined to obtain a virtual environment model. The main advantage of the developed program is the possibility of adaptive adjustment of image parameters depending on environmental conditions. The program also implements the function of switching information channels to display data from various devices. The use of augmented reality technologies in intelligent vehicle control systems contributes not only to the convenience of car operation, but also improves the comfort of travel conditions, increases the level of driving safety.

About the authors

Larisa V. Kruglova

Peoples’ Friendship University of Russia (RUDN University)

Email: kruglova-lv@rudn.ru
ORCID iD: 0000-0002-8824-1241

Candidate of Technical Sciences, Associate Professor of the Department of Mechanics and Control Processes, Academy of Engineering

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Fafa K. Ceesay

Peoples’ Friendship University of Russia (RUDN University)

Author for correspondence.
Email: 1032199266@rudn.ru
ORCID iD: 0000-0001-6762-9231

master student, Department of Mechanics and Control Processes, Academy of Engineering

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

References

  1. Dini G, Mura MD. Application of augmented reality techniques in through-life engineering services. Procedia CIRP. 2015;38:14-23. https://doi.org/10.1016/j.procir.2015.07.044
  2. Daponte P, De Vito L, Picariello F, Riccio M. State of the art and future developments of the augmented reality for measurement applications. Measurement. 2014;57:53-70. https://doi.org/10.1016/j.measurement.2014.07.009
  3. Milgram P, Kishino F. A taxonomy of mixed reality visual displays. IEICE Transactions on Information and Systems. 1994;E77-D:1321-1329.
  4. Jetter J, Eimecke J, Rese A. Augmented reality tools for industrial applications: what are potential key performance indicators and who benefits? Computers in Human Behavior. 2018;87:18-33. https://doi.org/10.1016/j.chb.2018.04.054
  5. Martinetti A, Marques H, Singh S, Dongen L. Reflections on the limited pervasiveness of augmented reality in industrial sectors. Applied Sciences. 2019;9:3382. https://doi.org/10.3390/APP9163382
  6. Cardoso LF, Mariano FC, Zorzal ER. A survey of industrial augmented reality. Computers & Industrial Engineering. 2020;139:106159. https://doi.org/10.1016/j.cie.2019.106159
  7. Masood T, Egger J. Augmented reality in support of Industry 4.0 - implementation challenges and success factors. Robotics and Computer-Integrated Manufacturing. 2019;58:181-195. https://doi.org/10.1016/j.rcim.2019.02.003
  8. Egger J, Masood T. Augmented reality in support of intelligent manufacturing - a systematic literature review. Computers & Industrial Engineering. 2020;140:106195. https://doi.org/10.1016/j.cie.2019.106195
  9. Gattullo M, Scurati GW, Fiorentino M, Uva AE, Ferrise F, Bordegoni M. Towards augmented reality manuals for industry 4.0: a methodology. Robotics and Computer-Integrated Manufacturing. 2019;56:276-286. https://doi.org/10.1016/j.rcim.2018.10.001
  10. Arnaldi B, Guitton P, Moreau G. Virtual reality and augmented reality: myths and realities. Hoboken: ISTE Ltd, John Wiley & Sons; 2018.
  11. Lima JP, Roberto R, Simoes F, Almeida M, Figueiredo L, Teixeira JM, Teichrieb V. Markerless tracking system for augmented reality in the automotive industry. Expert Systems with Applications. 2017;82:100-114. https://doi.org/10.1016/j.eswa.2017.03.060
  12. Gay-Bellile V, Bourgeois S, Tamaazousti M, Naudet-Collette S, Knodel S. A mobile markerless augmented reality system for the automotive field. Proceedings of the IEEE ISMAR 2012 Workshop on Tracking Methods and Applications, Atlanta, GA, USA, 5-8 November 2012. Atlanta; 2012.
  13. Halim AZ. Applications of augmented reality for inspection and maintenance process in automotive industry. Journal of Fundamental and Applied Sciences. 2018;10: 412-421.
  14. Lundquist C, Schön T. Estimation of the free space in front of a moving vehicle. SAE Technical Paper. 2009-01-1288. https://doi.org/10.4271/2009-01-1288
  15. Zhenhai G, Bing W. An adaptive PID controller with neural network self tuning for vehicle lane keeping system. SAE Technical Paper. 2009-01-1482. https://doi.org/10.4271/2009-01-1482
  16. Li J, Yang X, Wang ZH, Miao H. Research of three anti-lock braking control algorithms to enhance the effect of vehicle directional stability. Applied Mechanics & Materials. 2014;742:618-624. https://doi.org/10.4028/www.scientific.net/AMM.742.618
  17. Jin H, Li Sh. Research on stability control based on the wheel speed difference for the AT vehicles. Discrete Dynamics in Nature & Society. 2015;2015:251207. https://doi.org/10.1155/2015/251207
  18. Surkova NE, Ostroukh AV, Eremina TI. Professional information systems and databases: guidelines for laboratory work. Krasnoyarsk: Science and Innovation Center Publishing House. 2015. (In Russ.) https://lib.madi.ru/fel/fel1/fel16M490.pdf

Copyright (c) 2022 Kruglova L.V., Ceesay F.K.

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