Modular Architecture of Advanced Driver Assistance Systems for Effective Traffic Sign Recognition
https://doi.org/10.25205/1818-7900-2023-21-3-56-71
Abstract
Analysis of modern approaches to the implementation of driver assistance systems, as well as the implementation of the architecture of the driver assistance system, aimed at recognizing traffic signs at the maximum distance from it under difficult weather conditions, for early feedback to the driver. The paper considers the main signals used in the implementation and operation of the driver assistance system: data from the car's CAN bus, information from a GPS receiver, video fragments from a digital camera. The presented modular architecture uses the listed data sources for estimating the traffic situation, as well as neural network methods for recognizing traffic signs. The modular architecture of the driver assistance system is presented, which allows notifying the driver about traffic signs. The system is equipped with lane boundary control to alert the driver to signs related to the adjacent carriageway when turning. It has been experimentally proven that the modular architecture of the driver assistance system presented in the paper is not inferior in speed and accuracy to alternative systems, acting as a comprehensive autonomous solution.
Keywords
advanced driver assistance systems,
lane-keep assistance,
traffic sign recognition,
artificial neural network architectures
Supporting agencies: The study was supported by a grant for the implementation of the program of strategic academic leadership "Priority-2030" within the framework of the research project "Pr2030-MON science BCh/SP2/B/19b".
About the Authors
I. K. Kharchenko
Tomsk State University of Control Systems and Radioelectronics
Russian Federation
Russian Federation
Kharchenko Igor Konstantinovich, Postgraduate Student
Tomsk
I. G. Borovskoy
Tomsk State University of Control Systems and Radioelectronics
Russian Federation
Russian Federation
Borovskoy Igor Georgievich, Doctor of Physics and Mathematics
Tomsk
E. А. Shelmina
Tomsk State University of Control Systems and Radioelectronics
Russian Federation
Russian Federation
Shelmina Elena Aleksandrovna, Candidate of Physics and Mathematics, Associate Professor
Tomsk
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For citations:
Kharchenko I.K., Borovskoy I.G., Shelmina E.А. Modular Architecture of Advanced Driver Assistance Systems for Effective Traffic Sign Recognition. Vestnik NSU. Series: Information Technologies. 2023;21(3):56-71. (In Russ.) https://doi.org/10.25205/1818-7900-2023-21-3-56-71
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