Authors S.M. Sokolov, A.A. Boguslavsky
Month, Year 02, 2016 @en
Index UDC 681.518.3: 629.058: 629.053
Abstract In work process of working out and configuration of mobile means information support systems with the raised degree of autonomy and completely autonomous is analyzed. Modern lines in creation of similar systems are considered, examples from world and domestic experience of working out are resulted. For increase of efficiency of creation of mobile means intellectual autopilots processes it is offered to enter similar systems configurations space into consideration. Space axes are: hardware/sensor, functioning environment models and algorithmic maintenance. Points of this space are «assemblage points» concrete applied systems of information support as a part of control systems of mobile means. The configuration space allows spreading out structurally process of information support systems creation to components, qualitatively and quantitatively by means of conformity matrixes to estimate possible variants of concrete system configuration. Recommendations are made and decisions on components on each configuration space axes are offered. Formation bases of "a coordination cube» hardware-software information support systems configurations as a part of intellectual control systems of mobile means are put. In approaches to efficiency increase, both the process of creation, and resultants of systems accents become on use as the information systems basis of multipurpose real time vision system and hardware-software unification of offered decisions. On hardware components configurations a real time vision system in detail understand. Construction of models of an external world representation is offered to be based on the interpreting navigation concept. In algorithmic maintenance association differently levels processing algorithms is offered to be realized on the basis of large-scale patterns and unification of program realizations. Examples of author"s use of the described approaches and decisions are resulted.

Download PDF

Keywords Autonomous vehicles; information support system; real time vision system; software pattern; interpreting navigation; configuration space.
References 1. Buehler M. et al. The 2005 DARPA Grand Challenge. Springer, 2007.
2. Buehler M. et al. The DARPA Urban Challenge. Springer, 2009.
3. Sayt proekta «samoupravlyaemyy» avtomobil' (self-driving car) [The website of the project "driverless" car (self-driving car)]. Available at:
4. John Markoff (October 9, 2010). "Google Cars Drive Themselves, in Traffic". The New York Times. Retrieved October 11, 2010.
5. Soobshchenie agentstva Defence Talk [The Agency Defence Talk]. Available at: Oshkosh.
6. Ford pervym testiruet avtonomnye avtomobili v usloviyakh snega. Razdel «novosti» ofitsial'nogo sayta kompanii Ford Motor Company [Ford первым тестирует автономные автомобили в условиях снега. Раздел «новости» официального сайта компании Ford Motor Company]. Available at:
7. Maksim Kadakov. Pereschityvaem kamery samoupravlyaemogo Nissan Leaf. Za rulem. RF [Recalculate camera driverless Nissan Leaf. Behind the wheel. Of the Russian Federation]. Available at:
8. Bespilotnyy kamaz gotov k ispytaniyam. Razdel «novosti» ofitsial'nogo sayta kompanii KAMAZ [Unmanned KAMAZ is ready for testing. News section of the official website of KAMAZ]. Available at:
9. Ellery A. Rover vision – fundamentals / Planetary Rovers, Springer, 2016.
10. SShA zavershili palubnye ispytaniya bespilotnika X-47B. Novostnoe agentstvo [The U.S. has finished testing the deck drone X-47B. News Agency]. Available at:
11. Kompanii Ford i Google ob"edinyat usiliya po avtonomnym mashinam. Automotive News [Ford and Google will join forces for Autonomous vehicles. Automotive News]. Available at:
12. Anatoliy Alizar. Bespilotnye «Kamazy» vyezzhayut na dorogi obshchego naznacheniya. Geektimes [Unmanned KAMAZ trucks go on roads of General purpose. Geektimes]. Available at:
13. Krasnobaev A.A. Obzor algoritmov detektirovaniya pr.ostykh elementov izobrazheniya i analiz vozmozhnosti ikh apparatnoy realizatsii [An overview of detection algorithms etc. chili of image elements and analyze the possibility of their hardware implementation], Preprint IPM im. M.V. Keldysha RAN [Preprint IPM im. M. V. Keldysh Russian Academy of Sciences], 2005, No. 114, 20 p. Available at:
14. Vasilyev A.I., Boguslavskiy A.A., Sokolov S.M. Parallel SIFT-detector implementation for images matching, Proc. of the 21st Conference on Computer Graphics and Vision, GraphiCon’ 2011, September 26-30, 2011, Moscow, pp. 173-176.
15. Boguslavsky A.A., Sokolov S.M., Sazonov V.V. Computer Vision for Control and Research, of Mechanical Systems. Proc. of 8th Mechatronics Forum International Conference, University of Twente, Netherlands, June 24-26, 2002, pp. 1096-1105.
16. Boguslavsky A.A., Sokolov S.M., Trifonov O.V., Yaroshevsky V.S. Intellectual information system for mobile robot control, Proc. of the Intern. Conf. SCI2002, July 14-18, Orlando, Florida, USA, 2002.
17. Boguslavsky A.A., Sokolov S.M. Component Approach to the Applied Visual System Software Development, 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2003), July 27-30, Orlando, Florida, USA, 2003.
18. Laplante P. Software engineering for image processing systems. CRC Press, 2004.
19. Wagner M. et al. Principles of Computer System Design for Stereo Perception, Carnegie-Mellon University Technical Report, CMU-RI-TR-02-01, 2002.
20. Scaramuzza D., Fraundorfer F. Visual Odometry. Part I, IEEE Robotics and Automation Magazine, December 2011, pp. 80-92.
21. Scaramuzza D., Fraundorfer F. Visual Odometry. Part II, // IEEE Robotics and Automation Magazine, June 2012, pp. 78-90.
22. Rankin A. et al. Unmanned ground vehicle perception using thermal infrared cameras, Proc. SPIE Unmanned Systems Technology XIII, 2011(SPIE, 2011).
23. Sokolov S.M., Boguslavsky A.A. Intellectual Images Processing for a Realtime Recognition Problem, Proc. The 2nd Intern. Multi-Conf. on Complexity, Informatics and Cybernetics (IMCIC2011), Orlando, Florida, USA, March 27th-30th, 2011, Orlando, Florida, USA, 2011, Vol. II, pp. 406-411.
24. Sokolov S.M., Boguslavsky A.A., Sazonov V.V., Triphonov O.V. On-board navigation based on accelerometers and vision system, Proc. of the 14th Mechatronics Forum International Conference (Mechatronics 2014), Karlstad University, Sweden, June 16-18, 2014, pp. 175-181.
25. Sokolov S.M., Boguslavsky A.A. , Vasilyev A.I., Trifonov O.V. Development of software and hardware of entry-level vision systems for navigation tasks and measuring, Advances in Intelligent Systems and Computing (Springer), 2013, Vol. 208 AISC, pp. 463-476.
26. Sokolov S.M., Boguslavsky A.A., Platonov A.K., Kiy K.I., Gorelik L.I., Filachev А.М., Fumin А.I. An IR Channel-Based Automated Driver Assistance System, Proc. 12th Intern. Conf on Systemics, Cybernetics and Informatics (WMSCI 2008), Orlando, Florida, USA, June 29-July 2. 2008, Vol. III, pp. 368-373.
27. Sokolov S.M., Boguslavsky A.A., Kuftin F.A. Vision System for Relative Motion Estimation from Optical Flow. Proc. 13th Intern. Conf on Systemics, Cybernetics and Informatics (WMSCI 2009), Orlando, Florida, USA, July 10-13, 2009.
28. Baranov, S.N., Nikiforov V.V. Density of Multi-Task Real-Time Applications, Conference of Open Innovation Association, FRUCT, 2015-June (June), pp. 9-15.
29. Baranov S.N., Telezhkin A.M. Metrics for Software Development, SPIIRAS Proceedings, 2014, Issue 5 (36), pp. 5-27.
30. Shin B.-K., Xu Z., Klette R. Visual lane analysis and higher-order tasks: a concise review, Machine Vision and Applications, April 2014.
31. Hillel A., Lerner R., Levi D., Raz G. Recent progress in road and lane detection: a survey, Machine Vision and Applications, February 2012.
32. Tumofte R., Zimmermann K., Van Gool L. Multi-view traffic sign detection, recognition, and 3D localization, Machine Vision and Applications, April 2014, Vol. 25 (3), pp. 633-647.
33. Bak A., Bouchafa S., Aubert D. Dynamic objects detection through visual odometry and stereo-vision: a study of inaccuracy and improvement sources, Machine Vision and Applications, April 2014, Vol. 25 (3), pp. 681-697.
34. Maimone M. et al. Autonomous Navigation Results from the Mars Exploration Rover (MER) Mission, Experimental Robotics IX, STAR 21, Springer-Verlag, 2006, pp. 3-12.
35. Johnson A. et al. Design Through Operation of an Image-Based Velocity Estimation System for Mars Landing, International Journal of Computer Vision, 2007, No. 74 (3), pp. 319-341.
36. Howard T. et al. Enabling Continues Planetary Rover Navigation through FPGA Stereo and Visual Odometry, IEEE Aerospace Conference, Big Sky, Montana, 2012.
37. Matthies L. et al. Computer Vision on Mars, International Journal of Computer Vision, October 2007, No. 75 (1), pp. 67-92.
38. Ellery A. Rover vision – fundamentals, Planetary Rovers. Springer, 2016, pp. 199-262.

Comments are closed.