Adaptive Software Complex for Car Navigation System
Abstract
This article presents the software complex structure of autonomous vehicle navigation system. Navigation algorithms are based on different linearizations of Kalman filter, including perturbation Kalman filter, extended Kalman filter, iterated extended Kalman filter. Navigation system is a multi-mode and can vary accuracy and system CPU load by switching between modes. Different modes correspond to different implementations of the Kalman filter, as well as different motion models. Various implementations of the Kalman filter are used to select estimation accuracy and current CPU load. Different motion models are used to allow system to react instantly to changes in the environment. All switching happens in real time and do not require additional CPU calculations. The system architecture is described using the Unified Modeling Language (UML) and can be implemented in most object-oriented programming languages.
About the Author
A. N. Zabegaev
Keldysh Institute of Applied Mathematics (Russian Academy of Sciences)
Russian Federation
Russian Federation
References
1. Павловский В. Е., Огольцов В. Н. Динамическая модель механической трансмиссии автомобиля // Спецтехника и связь. 2012. № 5-6. С. 27-36.
2. Павловский В. Е., Огольцов В. Н., Огольцов Н. С. Микрокомпьютерная система управления нижнего уровня для автомобиля с механической трансмиссией // Мехатроника, автоматизация, управление. 2014. № 6. С. 29-36.
3. Basarke C., Berger C., Rumpe B. Software & Systems Engineering Process and Tools for the Development of Autonomous Driving Intelligence // J. Aerosp. Comput., Inf., Commun. 2007, N. 4. P. 1158-1174.
4. Atul Kanaujia, Tae Eun Choe, Hongli Deng. Complex Events Recognition under Uncertainty in a Sensor Network. 2014. URL: http://arxiv.org/pdf/1411.0085.pdf.
5. Kurdej M., Moras J., Cherfaoui V., Bonnifait Ph. Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids // EEE/RSJ International Conference on Intelligent Robots and Systems. 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Tokyo, Japan. 2013.
6. Забегаев А. Н., Калиниченко А. В., Павловский В. В., Павловский В. Е. Объединенная система навигации мобильного робота по маякам и видеоориентирам // Мехатроника, автоматизация, управление. 2011. № 10. С. 66-70.
7. Kalman R. A new approach to linear Filtering and prediction problems // Transactions ASME Journal of Basic Engineering. 1960. N. 82.
8. Grewal M., Andrews A. Kalman Filtering: theory and practice. NJ, USA: Prentice-Hall, Inc. Upper Saddle River, 1993.
9. Maybeck P. S. Stochastics Models, Estimation, and Control: Introduction. New York: ACADEMIC PRESS, INC, 1979.
10. OMG Unified Modeling Language (OMG UML), Infrastructure v2.4.1, 2011. URL: http://www.omg.Org/spec/UML/2.4.1/Infrastructure/PDF
Review
For citations:
Zabegaev A.N. Adaptive Software Complex for Car Navigation System. Mekhatronika, Avtomatizatsiya, Upravlenie. 2015;16(2):140-144. (In Russ.)
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