Implementation of Secure Traffic Light Management Using a Neuromorphic Computing Base Based on Fuzzy Graphs

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Аннотация

The task of safe traffic light management is to normalize traffic. Secure management involves the implementation of the protection of information involved in the management process. The traffic light management process is a complex dynamic process. The control object is a set of traffic lights. The subject of management is a specific management system, which can be autonomous or part of a top-level management system. The implementation of a traffic light based on a neuromorphic computing base allows you to perform some of the control processes in automatic mode. As part of the control process, you have to deal with different types of data (sensor readings, control signals of different levels, etc.). Big data is processed throughout the entire control process, and one of the main tasks is to reduce the dimensionality of the information being processed. The successful solution of this problem directly depends on the model of organization of a set of traffic lights. The article discusses various traffic lights, each of which is equipped with an intelligent controller. The neuromorphic basis of the intelligent controller allows you to expand the capabilities of the computing base at a low level. Fuzzy graphs are used to represent a set of traffic lights and to connect the traffic lights to the control system. This model makes it possible to combine the information and control components of the process of interaction between the object and the subject of management into one whole. The advantages of this representation are the minimization of information necessary for the successful solution of the problem of safe management, and the expansion of the possibilities of the management process through the use of fuzzy information.

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Авторлар туралы

Alexandra Volosova

Bauman Moscow State Technical University

Email: volosova@bmstu.ru
ORCID iD: 0000-0002-3817-2671
SPIN-код: 7973-5425
Scopus Author ID: 57437351400
ResearcherId: T-1829-2017

Cand. Sci. (Eng.), Associate Professor

Ресей, Moscow

Ekaterina Matyukhina

Moscow Automobile and Road State Technical University

Хат алмасуға жауапты Автор.
Email: makaterina_ski@mail.ru
SPIN-код: 7078-2329
Scopus Author ID: 56538724100

Cand. Sci. (Eng.), Associate Professor

Ресей, Moscow

Egor Morozov

Moscow Automobile and Road State Technical University

Email: extremebupel@gmail.com
Ресей, Moscow

Әдебиет тізімі

  1. Morozov E.A., Volosova A.V. Implementation of intelligent automatic traffic control in urban control zones based on the use of fuzzy models. Computational Nanotechnology. 2023. Vol. 10. No. 4. Pp. 39–45. (In Rus.). doi: 10.33693/2313-223X-2023-10-4-39-45. EDN: FXSDTS.
  2. Volosova A.V. The use of a tensor model for processing uncertainty in complex dynamical systems. Computational Nanotechnology. 2023. Vol. 10. No. 1. Pp. 79–87. (In Rus.). doi: 10.33693/2313-223X-2023-10-1-79-87. EDN: UEVWUE.
  3. Maksimychev O.I., Mezentsev K.N., Volosova A.V. Information and communication technologies and elements of artificial intelligence in intelligent transport systems. World of Transport and Technological Machines. 2023. No. 1-1 (80). Pp. 112–118. (In Rus.). doi: 10.33979/2073-7432-2023-1(80)-1-112-118. EDN: FZKCDR.
  4. Maksimychev O.I., Volosova A.V., Mezentsev K.N. et al. The use intelligent electronic hitch for vehicle management. In: Intelligent technologies and electronic devices in vehicle and road transport complex. Conference Proceedings TIRVED 2021. doi: 10.1109/TIRVED53476.2021.9639222. EDN: LRNJVX.
  5. Volosova A.V., Yurchik P.F., Golubkova V.B. et al. Using a digital twin in ultra-large-scale System. In: Intelligent technologies and electronic devices in vehicle and road transport complex. Conference Proceedings TIRVED 2023. doi: 10.1109/TIRRED58506.2023.10332708.

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Әрекет
1. JATS XML
2. Fig. 1. а – fuzzy Berge graph; b – Berge graph

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3. Fig. 2. Traffic signal intersection

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4. Fig. 3. Fuzzy veriables normal and abnormal

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5. Fig. 4. Fuzzy graph-based control model

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6. Fig. 5. Integration of the traffic signal control system into the “Smart town system”

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7. Fig. 6. Integration of the traffic signal control system into the “Traffic Management system”

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