Methods of telematics data analysis for decision support systems for optimal unmanned vehicle control

The purpose of the research is purpose of the study. The analysis of modern solutions aimed at supporting decision-making on optimal driving within the framework of the Connected Car concept, as well as systematize the main methods of using telematics data and architecture of similar systems. 
Methods. Methods. The study  is based on an analysis of domestic and  foreign publications, patents and practical implementations in the field of Connected Cars, as well as examples of the introduction of telematics platforms in the automotive industry. Classical statistical methods, machine learning algorithms, and big data streaming tools are considered. Special attention is paid to scalability, standardization and quality of telematics information. 
Results. It has been established that most modern systems rely on basic statistical and machine  learning methods (classification, clustering, regression models) to analyze large amounts of data on vehicle movement. However, unified approaches to the integration of these methods into the integrated architecture of decision support systems have not yet been formed. Hybrid approaches combining statistical methods, ML algorithms, and Big Data technologies demonstrate the greatest effectiveness. Their widespread adoption is hampered by the lack of uniform standards for telematics data exchange, the difficulties of reliable data storage, and the need to filter noise and omissions. Based on the review, the advantages and disadvantages of various methods are identified, as well as the requirements for the architecture of the DSS for the Connected Car are formulated. 
Conclusion. The review confirms the high demand for  flexible, scalable solutions capable of processing  telematics data in real time and taking into account individual driving characteristics. The further development of such systems is closely related to the unification of telematics information formats, increased security (both in terms of data protection and in the field of traffic), as well as expanding the range of analyzed sources (road infrastructure, weather conditions, smart city ecosystems, etc.) to improve the accuracy of recommendations and optimize driving. 

1. Khakimov Sh.K., Samatov R.G., Razhapova S.S., Abdurazzakova D.A. Telematics in transport. Ekonomika i sotsium = Economics and Society. 2022;(10):671–677. (In Russ.)

2. Doronenkova A.A., Shirokov I.B. Fundamentals of construction and prospects for the development of Connected Car technology. In: Afonin I.L. (ed.) Sovremennye problemy radioelektroniki i telekommunikatsii: sbornik nauchnykh trudov = Modern problems of radio electronics and telecommunications: collection of scientific papers. N 5. Moscow: RNTORES im. A.S. Popova; 2022. P. 200. (In Russ.)

3. Guillen M., Nielsen J.P., Ayuso M. The use of telematics devices to improve au-tomobile insurance rates. Risk Analysis. 2019;(39):662–672 https://doi.org/10.1111/risa.13172

4. Kushelev I.Y. Introduction of innovative information technologies in the insurance market in Russia: telematics in auto insurance. Putevoditel' predprinimatelya = Entrepreneur's Guide. 2023;16(2):110–119. (In Russ.) https://doi.org/10.24182/2073-9885-2023-16-2-110-119

5. Zaineb Chelly Dagdia, Christine Zarges, Gaël Beck, Hanene Azzag, Mustapha Lebbah. A Distributed Rough Set Theory Algorithm based on Locality Sensitive Hashing for an Efficient Big Data Pre-processing. In: IEEE International Conference on Big Data (Big Data). Seattle, WA: IEEE; 2018. P. 4266–4273. https://doi:10.1109/BigData.2018.8622024

6. Sterk F., Stocker A., Heinz D., Weinhardt Ch. Unlocking the value from car data: a taxonomy and archetypes of connected car business models. Electronic Markets. 2024;34:1. https://doi.org/10.1007/s12525-024-00692-5

7. Rozov A.A., Stepanova A.A., Komarova M.V., Solodkova E.V. Connected Cars market: Current state and main development prospects. Vestnik Altaiskoi akademii ekonomiki i prava = Bulletin of the Altai Academy of Economics and Law. 2021;(6):90–95. (In Russ.) https://doi.org/10.17513/vaael.1733

8. Postolit A.V. Prospects for the application of artificial intelligence and computer vision in transport systems and connected cars. Mir transporta. Rossiiskii universitet transporta (MIIT) = The world of transport. Russian University of Transport (MIIT). 2021;19(1):74–90. (In Russ.)

9. Lashkov I.B. An approach to recognizing the driving style of a vehicle driver based on the use of smartphone sensors. Informatsionno-upravlyayushchie sistemy = Information and Control Systems. 2018;(5):2–12. (In Russ.) https://doi.org/10.31799/1684-8853-2018-5- 2-12

10. Mandala V., Surabhi S.N.R.D. Integration of AI-driven predictive analytics in-to connected car platforms. International Advanced Research Journal in Science, Engineering and Technology. 2024;7:84–93. https://doi.org/10.17148/iarjset.2020.71216

11. Qian Li, Pan Chen, Rui Wang. Edge Computing for Intelligent Transportation System: A Review. In: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Beijing, China. Pt. 2. Singapore: Springer; 2019. P. 130–137. http://doi.org/10.1007/978-981-15-1925-3_10

12. Parmar Y., Natarajan S., Sobha G. DeepRange: Deep-learning based object de-tection and ranging in autonomous driving. IET Intelligent Transport Systems. 2019;13(8):1256–1264.

13. Lyapin S.A., Kapsky D.V., Rizayeva Yu.N., Kadasev D.A. Connected cars in intelligent transport systems of smart cities. In: Infokommunikatsionnye i intellektual'nye tekhnologii na transporte: sbornik statei Mezhdunarodnoi nauchno-prakticheskoi konferentsii = Infor-mation and communication and intelligent technologies in transport: Collection of articles of the International scientific and practical Conference. Lipetsk: Lipetskii gosudarstvennyi tekhnicheskii universitet; 2022. P. 7–14. (In Russ.)

14. Okolnishnikova I.Yu., Keller A.V., Konks V.Ya. Marketing analysis of the global market of telematics transport and information management systems. Vestnik universiteta = Bulletin of the University. 2022;(12):46–54. (In Russ.)

15. Permovsky A.A., Uskova A.A. Evaluation of the effectiveness of the implementation of Connected Car technology. International Journal of Advanced Studies = International Journal of Advanced Studies. 2021;11(3):68–75. (In Russ.) https://doi.org/10.12731/2227-930X-2021-11-3-68-75

16. Permovsky A.A., Uskova A.A. Evaluation of the effectiveness of the implementation of Connected car technology. International Journal of Advanced Studies = International Jour- nal of Advanced Studies. 2021;11(3):68–75. (In Russ.) https://doi.org/10.12731/2227-930X-2021-11-3-68-75

17. Postolit A.V. Promising services for connected cars based on neural network technologies and computer vision systems. Novosti navigatsii = Navigation News. 2021;(1):28–34. (In Russ.)

18. Sanjay P. Pande, Sarika Khandelwal. An Improved Deep Network and Hand-crafted Feature-Based Scene Classification Convolutional Model for Self-Driving Cars. Communica-tions on Applied Nonlinear Analysis. 2024;31(2s):593–605.

19. Hassan M., Hussain A., Anwar S. Challenges and Solutions in Integrating AI-Driven Predictive Analytics into Connected Car Platforms. IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD). Chengdu, China: IEEE; 2018. P. 153–157. https://doi.org/10.1109/ICAIBD.2018.8372276

20. Gromov N.D., Saprykin D.A. The use of Internet of Things technologies in self-driving cars and further development paths. Moya professional'naya kar'era = My Professional Career. 2021;1(24):109–115.