Safety model construction for a complex automatic transportation system

Abstract

The Aim of the paper is to consider approaches to the analysis of a safety model of complex multi-loop transportation systems comprising not completely supervised subsystems. Method. For the description of a safety model, the paper uses systems theoretic process analysis (STPA) methods and the principles specified in ISO/PAS 21448:2019 (SOTIF). Result. The paper shows drawbacks of the FTA and FMEA local risk analysis methods and demonstrates a demand for some universal approach based on the combination of STPA and control theory. It gives an overview of the major stages of such analysis for the safety model of complex transportation systems exemplified by the Moscow Central Circle, which provide a feedback for safety evaluation of a transport control system under development. The paper analyzes the feasibility of using a virtual model for control purposes in the form of a so-called “supervised artificial neural network”.Conclusion. Today, railways are actively testing autonomous systems (with no driver onboard) that apply as their subsystems automatic perception modules using machine learning. The introduction of the latter into the control loop complicates the task of hazard analysis and safety evaluation of such systems using conventional FTA and FMEA methods. The construction of a safety model of such complex multi-loop transportation systems comprising not completely supervised subsystems that use machine learning methods with not completely predictable behavior requires the application of a systems approach to the analysis of unsafe scenarios along with the compilation of a scenario library and the formalization of a hazard model’s description, pertaining to the boundaries of various control loops as well, in order to reduce the regions of unknown unsafe scenarios for autonomous transportation systems under development.

Evolution of Rail Operations Control Centres

“…Further evolution of Rail Operations Control Centres, taking into account the target state of ROCC 4.0, involves the automation of intelligent control functions, i.e. the implementation of real-time control systems based on artificial intelligence (machine learning), Big Data processing methods and predictive analytics [4]. ROCC 4.0 is in fact a control model with constant feedback, and the target state can be achieved by expanding the feedback and increasing the efficiency and reliability of information through the application of different control, monitoring and diagnostics tools, including mobile (for example, drones with computer vision and other tools for video and photo recording) and fixed devices (computer vision systems at passenger and freight stations, integrated checkpoints for diagnostic and monitoring rolling stock at marshalling yards, distributed acoustic sensing systems for fiber optic lines, etc.…”

Digital Twins for Trust Building in Autonomous Drones through Dynamic Safety Evaluation

“…This happens because the strategy to compute an accident probability requires the exclusion of critical causal factors that are not stochastic or do not have probabilistic data. Therefore, STPA aims to investigate unsafe control actions by identifying the path to accidents or risk scenarios (Ozerov et al, 2021). Then, it incorporates causal factors that traditional analysis techniques do not fully consider, such as: uncontrolled component interactions, incorrect requirements, insufficient coordination between multiple controllers, and poor decision management (Plioutsias et al, 2018).…”