Big Data-based methods for functional safety case preparation

Aim. The paper aims to overview the opportunities, approaches and techniques of studying and ensuring functional safety of transportation systems, including those driverless, with the use of Big Data. It is noted, that the modern technology that underpins next-generation transportation systems that operate in ever-evolving conditions, with significant numbers of passengers, requires modified control systems design. With the growth of agglomerations, many suburban systems merge with urban ones, and their traffic intervals are close in size to those of the metro. Under these conditions, there is a transition from human-machine systems to automatic systems, characterized by varying degrees of automation. Widespread deployment of digital telecommunications, process automation and remote data collection and management technology is under way in railway transportation. Variations in the behaviour of transportation systems as a type of cyberphysical system cause a paradigm shift from line-andstaff to adaptive management with fundamentally non-linear systems with variable structure and parameters.

Methods. Control and management systems are conventionally assessed for Lyapunov’s stability. In this case, the behaviour of a stable system can with a 100% probability be predicted in the neighbourhood of the ε-tube. For the examined supervised systems, in which stability is ensured through the introduction of a supervisor algorithm, speaking of a strict Lyapunov’s stability would not be correct. The idea of controlled algorithms can extend not only to ANN, but also to other intelligent algorithms. Thus, a scope of systems and knowledge is identified that is not covered by the relevant regulatory documents and methods of safety case preparation. Identifying and eliminating abnormal signals of such systems would allow defining the boundaries of the set of acceptable processes more clearly, thus, in some cases, increasing the speed of the decision algorithms by disabling an entire branch of unfavourable scenarios.

Results. For non-linear transportation systems with variable stricture and parameters, examples are considered of machine learning/Big Data application in analysing the functional safety of complex control/management systems in railway transportation. The paper proposes the concept of application of supervised artificial neural networks combined with model checking. A special attention is given to artificial neural networks with control elements that are considered as a new subclass of neural networks.

Conclusion. Updated requirements are defined for transportation systems using artificial intelligence as part of adaptive train schedule management and autonomous train control. That will ultimately allow developing an entire line of research associated with the operation of complex systems with counterintuitive behaviour from AI-based system functional safety estimation and machine learning to safety case preparation of intelligent supervised control/management systems based on formal verification.

1. Gruntov P.S. [Centralized systems for automated railway management in market economy conditions]. Minsk: BelGUT; 2001. (in Russ.)

2. Osminin A.T. Problems and ways of their scientific solving on the issues of railroading. Bulletin of JSC RZD United Academic Council 2015;4:41-54. (in Russ.)

3. Fedukhin A.V. Mukha Ar.A., Sespedes Garsiya N.V. [Safety case of a computer system]. MMS 2016;3:93-101. (in Russ.)

4. Neema H. Simulation testbed for railway infrastructure safety and resilience evaluation. In: Proceedings of the 7th Symposium on Hot Topics in the Science of Security. Association for Computing Machinery; 2020. P 1–8.

5. Hadj-Mabrouk H. Contribution of Artificial Intelligence to Risk Assessment of Railway Accidents. Urban Rail Transit 2019;5:104–122.

6. Shubinsky I.B., Schäbe H., Rozenberg E.N. On the functional safety of a complex technical control system with digital twins. Dependability 2021;21(1):38-44.

7. Zhang J., Li J. Testing and verification of neural-network-based safety-critical control software: A systematic literature review. Information and Software Technology 2020;123.

8. Shubinsky I.B., Shäbe H., Rozenberg E.N. On the safety assessment of an automatic train operation system. Dependability 2021;21(4):31-37.

9. Ozerov A.V., Lysikov M.G., Olshansky A.M. Timetable as part of next-generation adaptive management system. Nauka i tekhnologii zheleznykh dorog 2021;5(1):50-64.

10. Ozerov A.V., Olshansky A.M. [Approaches to the assessment of the functional safety of a driverless automatic train control system]. In: International Scientific Conference Proceedings “Advanced Information Technologies and Scientific Computing”. Samara; 2021. P. 504-509. (in Russ.)

11. Gapanovich V.A., Shubinsky I.B., Zamyshlyaev A.M. Mathematical and information support of the URRAN system. Dependability 2013;1:12-19.

12. Ozerov A.V., Olshansky A.M. Safety model construction for a complex automatic transportation system. Dependability 2021;21(2):31-37.

13. Dorf R., Bishop R. Modern control systems. Moscow: Lab. Bazovykh Znaniy; 2004.

14. Rozenberg E.N. et al. [Hybrid neural network-based management of transportation systems]. Automation, Communications, Informatics 2017;12:2-5. (in Russ.)

15. Ignatenkov A.V., Olshansky A.M. About neural network error control an optimal control problem. Izvestiya Samarskogo nauchnogo centra Rossiyskoy akademii nauk 2016;18(4-4):733-738. (in Russ.)

16. Clarke E.M., Henzinger T.A., Veith H. et al. Handbook of model checking (Vol. 10). Cham: Springer; 2018.

17. Okhotnikov A.L., Popov P.A. Self-driving: yesterday, today and tomorrow. Automation, Communications, Informatics 2019;8:12-17. (in Russ.)

18. Flammini F. et al. A Vision of Intelligent Train Control. In: Proceedings of the 4th International Conference on Reliability, Safety and Security of Railway Systems (RSSRail-22). Springer LNCS; 2022. (preprint)