Abstract
The Intelligent Railway Driving Assistance System (IRDAS) is a novel kind of onboard system that relies on its own situational awareness function to ensure the safety and efficiency of train driving. In such systems, the use of situational awareness brings about a new fault-free safety problem, i.e., the safety of the intended functionality (SOTIF). It is essential to analyze the SOTIF-related hazardous factors for ensuring a safe train operation. In this paper, a hazard analysis approach is proposed to capture and evaluate SOTIF-related hazardous factors of IRDAS. This approach consists of an extended STPA-based hazardous factor identification part and a complex network-based hazardous factor evaluation part. In the first part, an extended control structure of STPA is designed for the modeling of the situational awareness process, followed by a new classification of SOTIF-related causal scenarios to assist the identification of causal scenarios. In the second part, a modeling method for heterogeneous complex networks and some customized topological indexes are proposed to evaluate the hazardous factors identified in the STPA causal analysis. The outcomes of the approach can help develop targeted hazard control strategies. The proposed approach has been applied to a new IRDAS operating in Tsuen Wan Line of Hong Kong MTR. The result shows that the approach is effective for the analysis of hazardous factors and is helpful for the formulation of hazard control strategies.
Highlights
The efficient and safe operation of railway systems relies on automated train control (ATC) systems based on the cooperation of onboard and ground-based signaling equipment
This paper aims to purpose a hazard analysis approach for the safety of the intended functionality (SOTIF) of the Intelligent Railway Driving Assistance System (IRDAS) using System Theoretic Process Analysis (STPA) and the complex network
To evaluate the hazardous factors identified by STPA, a network of hazardous factors and their causal relationships must be constructed first
Summary
The efficient and safe operation of railway systems relies on automated train control (ATC) systems based on the cooperation of onboard and ground-based signaling equipment. When ATC fails, the driver can only drive at an extremely limited speed, which brings about enormous disruption in the operation of the railway network. The Intelligent Railway Driving Assistance System (IRDAS), achieved by sensors such as cameras, RADARs and LiDARs, and AI algorithms, is being developed and installed on trains to make the train environment situationally aware by itself [1–3]. The driver can be allowed to operate the train at a much higher speed with the assistance of IRDAS. This causes a potential safety problem where the driver may not be able 4.0/).
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