INTELLIGENT AUTOMOTIVE SECURITY SYSTEMS BASED ON CLOUD ARCHITECTURES
Keywords:
cloud, microservice, serverless architecture, distributed application, security, road, car, intelligent system
Abstract
Intelligent automotive security systems based on cloud architectures are investigated in the work. The relationship between modern car safety systems and Internet of Things technology is shown. Modern intelligent automobile safety systems are characterized. The principles of formation of the main functional component systems are disclosed. Serverless computing has proven to be a significant shift in the way developers build and deploy applications. It is emphasized that by abstracting the underlying infrastructure, serverless computing allows developers to focus on writing code and creating functionality. Despite some limitations, the advantages of serverless computing, including scalability, cost-effectiveness, and flexibility, make it an attractive option for many automotive safety use cases. The categories of system users are characterized, these are system users, conformity assessment service providers, and system platform administrators. The principles of formation of microservices are described, it is noted that in the car safety system, the results of the limited context directly inform the microservices unit. The final configuration of microservices consists of three main domains: the user domain, the implementation domain, and the rule domain. The complex architecture of the platform is formed, which is presented graphically with the separation of the two main components of the frontend and backend, the structure of the multi-level logic of service provision and the flow of data in the system are described. It is noted that, taking into account the scale of modern intelligent car safety systems, the proposed architecture can be integrated into various systems, such as detection of malfunctions in the operation of the car, emergency control of the car, speed support system, etc. The process of system integration and the principle of operation using sensors of various directions are described in detail.Downloads
Download data is not yet available.
References
1. Коростельов М. В., Гнатов А. В. Дослідження активних систем безпеки для автотранспортних засобів / Автомобільний транспорт, 2020. вип. 46. С. 40-46. DOI: 10.30977/АТ.2219-8342.2020.46.0.40
2. Інтелектуальна транспортна система міста. Адаптивне керування рухом / Алексієв О.П., Алексієв В.О., Неронов С.М., Дьяков А.Е. // Матеріали конференції КІТ-2022, Харків, ХНАДУ, 2022. С. 134-136.
3. Бортові системи інтелектуального автомобіля / Мигаль В.Д., Бажинова Т.О., Іванов А.А. // Матеріали Всеукраїнської науково-практичної конференції «Автомобільний транспорт в аграрному секторі: проектування, дизайн та технологічна експлуатація» – 2019. Харків, 2019. С. 95-96.
4. Lin Dianchao, Li Li. (2023). An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals. IEEE Transactions on Vehicular Technology. PP. 1-13. 10.1109/TVT.2023.3257048.
5. Wang Yu, Wu Jie, He Hanfu. (2023). Research on Sound-Guided Design of Lane Departure Scenarios in Intelligent Car Cockpit. 10.1007/978-3-031-35678-0_13.
6. Song Dongjian, Zhu Bing, Zhao Jian, Han Jiayi, Chen Zhicheng. (2023). Personalized Car-Following Control Based on a Hybrid of Reinforcement Learning and Supervised Learning. IEEE Transactions on Intelligent Transportation Systems. PP. 1-16. 10.1109/TITS.2023.3245362.
7. Liu Yang, Fan Yingqi, Huang Darong, Mi Bo, Huang Liyuan. (2022). Formal Model and Analysis for the Random Event in the Intelligent Car with Stochastic Petri Nets and Z. Security and Communication Networks. 2022. 10.1155/2022/3288308.
8. Elsamadisy Omar, Shi Tianyu, Smirnov Ilia, Abdulhai Baher. (2023). Safe, Efficient, and Comfortable Reinforcement-Learning-Based Car- Following for AVs with an Analytic Safety Guarantee and Dynamic Target Speed. Transportation Research Record Journal of the Transportation Research Board. 10.1177/03611981231171899.
9. Chen Xingyu, Bai Haijian, Ding Heng, Gao Jianshe, Huang Wenjuan. (2023). A Safety Control Method of Car-Following Trajectory Planning Based on LSTM. Promet - Traffic&Transportation. 35. 380-394. 10.7307/ptt.v35i3.118.
10. Li Maosheng, Fan Jing, Lee Jaeyoung. (2023). Modeling Car-Following Behavior with Different Acceptable Safety Levels. Sustainability. 15. 6282. 10.3390/su15076282.
11. Wang Shufeng, Wang Xinkai, Wang Shihao. (2022). Lane Change Decision and Trajectory Planning for Intelligent Cars in Curved Road Scenarios. 10.3233/ATDE221112.
12. Peng Liqun, Huang Ju, Zhou Tuqiang, Xu Shucai. (2023). V2V‐enabled cooperative adaptive cruise control strategy for improving driving safety and travel efficiency of semi‐automated vehicle fleet. IET Intelligent Transport Systems. n/a-n/a. 10.1049/itr2.12402.
13. Kapadia Riya, Mehta Kush. (2023). Advancing Autonomous Navigation: Deep Learning Techniques for Self-Driving Cars. International Journal for Research in Applied Science and Engineering Technology. 11. 1863-1869. 10.22214/ijraset.2023.55491.
2. Інтелектуальна транспортна система міста. Адаптивне керування рухом / Алексієв О.П., Алексієв В.О., Неронов С.М., Дьяков А.Е. // Матеріали конференції КІТ-2022, Харків, ХНАДУ, 2022. С. 134-136.
3. Бортові системи інтелектуального автомобіля / Мигаль В.Д., Бажинова Т.О., Іванов А.А. // Матеріали Всеукраїнської науково-практичної конференції «Автомобільний транспорт в аграрному секторі: проектування, дизайн та технологічна експлуатація» – 2019. Харків, 2019. С. 95-96.
4. Lin Dianchao, Li Li. (2023). An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals. IEEE Transactions on Vehicular Technology. PP. 1-13. 10.1109/TVT.2023.3257048.
5. Wang Yu, Wu Jie, He Hanfu. (2023). Research on Sound-Guided Design of Lane Departure Scenarios in Intelligent Car Cockpit. 10.1007/978-3-031-35678-0_13.
6. Song Dongjian, Zhu Bing, Zhao Jian, Han Jiayi, Chen Zhicheng. (2023). Personalized Car-Following Control Based on a Hybrid of Reinforcement Learning and Supervised Learning. IEEE Transactions on Intelligent Transportation Systems. PP. 1-16. 10.1109/TITS.2023.3245362.
7. Liu Yang, Fan Yingqi, Huang Darong, Mi Bo, Huang Liyuan. (2022). Formal Model and Analysis for the Random Event in the Intelligent Car with Stochastic Petri Nets and Z. Security and Communication Networks. 2022. 10.1155/2022/3288308.
8. Elsamadisy Omar, Shi Tianyu, Smirnov Ilia, Abdulhai Baher. (2023). Safe, Efficient, and Comfortable Reinforcement-Learning-Based Car- Following for AVs with an Analytic Safety Guarantee and Dynamic Target Speed. Transportation Research Record Journal of the Transportation Research Board. 10.1177/03611981231171899.
9. Chen Xingyu, Bai Haijian, Ding Heng, Gao Jianshe, Huang Wenjuan. (2023). A Safety Control Method of Car-Following Trajectory Planning Based on LSTM. Promet - Traffic&Transportation. 35. 380-394. 10.7307/ptt.v35i3.118.
10. Li Maosheng, Fan Jing, Lee Jaeyoung. (2023). Modeling Car-Following Behavior with Different Acceptable Safety Levels. Sustainability. 15. 6282. 10.3390/su15076282.
11. Wang Shufeng, Wang Xinkai, Wang Shihao. (2022). Lane Change Decision and Trajectory Planning for Intelligent Cars in Curved Road Scenarios. 10.3233/ATDE221112.
12. Peng Liqun, Huang Ju, Zhou Tuqiang, Xu Shucai. (2023). V2V‐enabled cooperative adaptive cruise control strategy for improving driving safety and travel efficiency of semi‐automated vehicle fleet. IET Intelligent Transport Systems. n/a-n/a. 10.1049/itr2.12402.
13. Kapadia Riya, Mehta Kush. (2023). Advancing Autonomous Navigation: Deep Learning Techniques for Self-Driving Cars. International Journal for Research in Applied Science and Engineering Technology. 11. 1863-1869. 10.22214/ijraset.2023.55491.
Published
2023-12-12
How to Cite
Polozhyi Denys Intelligent automotive security systems based on cloud architectures / Denys Polozhyi, Oleksandr Oriekhov // Control, Navigation and Communication Systems. Academic Journal. – Poltava: PNTU, 2023. – VOL. 4 (74). – PP. 91-95. – doi:https://doi.org/10.26906/SUNZ.2023.4.091.
Section
Information Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
