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A Hazard Analysis Approach for the SOTIF in Intelligent Railway Driving Assistance Systems Using STPA and Complex Network

Authors :
Shijie Zhang
Tao Tang
Jintao Liu
Source :
Applied Sciences, Vol 11, Iss 16, p 7714 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

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.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
16
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.42f535d6395c4e78847542122a37bae3
Document Type :
article
Full Text :
https://doi.org/10.3390/app11167714