Fault Tree Analysis And Risk Mitigation Strategies For Autonomous Systems Via Statistical Model Checking

2021 ◽  
Author(s):  
Ashkan Samadi ◽  
Marwan Ammar ◽  
Otmane Ait Mohamed
Keyword(s):  
Model Checking ◽  
Statistical Model ◽  
Risk Mitigation ◽  
Autonomous Systems ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Mitigation Strategies ◽  
Statistical Model Checking ◽  
Tree Analysis ◽  
Risk Mitigation Strategies

Analysis of Restricted-Speed Accidents Using Fault Tree Analysis

2018 ◽  
Author(s):  
Zhipeng Zhang ◽  
Xiang Liu ◽  
Zheyong Bian
Keyword(s):  
Risk Mitigation ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Mitigation Strategies ◽  
Quantitative Risk Assessment ◽  
Transportation Safety ◽  
Accurate Estimation ◽  
Tree Analysis ◽  
Rule Compliance ◽  
Compliance Problem

Restricted speed is the speed that permits stopping within one-half the range of vision, normally not exceeding 20 miles per hour. The occurrences of some severe accidents at restricted speeds have highlighted the importance of safety improvements in restricted-speed operations. The Federal Railroad Administration (FRA) has identified restricted-speed violations as a common rule compliance problem. Nevertheless, little prior research has been conducted on the analysis of train operations and safety risk under restricted speeds. This paper used Fault Tree Analysis to explore scenarios for restricted-speed operations to identify failure paths that lead to train accidents. Understanding restricted-speed train accident causal chains and corresponding structural representations of relevant contributory precursors can contribute to more accurate estimation of restricted-speed risk. Four recent restricted-speed accidents were studied based on the information from the National Transportation Safety Board (NTSB) and FRA. This study may serve as a reference leading to the further development of quantitative risk assessment and the evaluation of risk mitigation strategies for restricted-speed operations.

Safety analysis of autonomous systems by extended fault tree analysis

2007 ◽  
Vol 21 (2-3) ◽  
pp. 287-298 ◽  
Author(s):  
Jan Åslund ◽  
Jonas Biteus ◽  
Erik Frisk ◽  
Mattias Krysander ◽  
Lars Nielsen
Keyword(s):  
Autonomous Systems ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Safety Analysis ◽  
Tree Analysis

Fast Dynamic Fault Tree Analysis by Model Checking Techniques

2018 ◽  
Vol 14 (1) ◽  
pp. 370-379 ◽  
Author(s):  
Matthias Volk ◽  
Sebastian Junges ◽  
Joost-Pieter Katoen
Keyword(s):  
Model Checking ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Dynamic Fault Tree ◽  
Fast Dynamic

scholarly journals Formal Verification of Heuristic Autonomous Intersection Management Using Statistical Model Checking

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4506
Author(s):  
Aaditya Prakash Chouhan ◽  
Gourinath Banda
Keyword(s):  
Model Checking ◽  
Statistical Model ◽  
Formal Verification ◽  
Autonomous Vehicles ◽  
Timed Automata ◽  
Autonomous Systems ◽  
Statistical Model Checking ◽  
Stage Of Development ◽  
Verification Techniques ◽  
Intersection Management

Autonomous vehicles are gaining popularity throughout the world among researchers and consumers. However, their popularity has not yet reached the level where it is widely accepted as a fully developed technology as a large portion of the consumer base feels skeptical about it. Proving the correctness of this technology will help in establishing faith in it. That is easier said than done because of the fact that the formal verification techniques has not attained the level of development and application that it is ought to. In this work, we present Statistical Model Checking (SMC) as a possible solution for verifying the safety of autonomous systems and algorithms. We apply it on Heuristic Autonomous Intersection Management (HAIM) algorithm. The presented verification routine can be adopted for other conflict point based autonomous intersection management algorithms as well. Along with verifying the HAIM, we also demonstrate the modeling and verification applied at each stage of development to verify the inherent behavior of the algorithm. The HAIM scheme is formally modeled using a variant of the language of Timed Automata. The model consists of automata that encode the behavior of vehicles, intersection manager (IM) and collision checkers. To verify the complete nature of the heuristic and ensure correct modeling of the system, we model it in layers and verify each layer separately for their expected behavior. Along with that, we perform implementation verification and error injection testing to ensure faithful modeling of the system. Results show with high confidence the freedom from collisions of the intersection controlled by the HAIM algorithm.

Risk Assessment in Bridge Construction Projects Using Fault Tree and Event Tree Analysis Methods Based on Fuzzy Logic

2015 ◽  
Vol 1 (3) ◽  
Author(s):  
Gholamreza Abdollahzadeh ◽  
Sima Rastgoo
Keyword(s):  
Fuzzy Logic ◽  
Traditional Approach ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Mitigation Strategies ◽  
Fuzzy Arithmetic ◽  
Event Tree ◽  
Event Tree Analysis ◽  
Tree Analysis ◽  
Risk Event

In this paper, interruption risk in construction activities of bridge projects is assessed in order to identify the main causes of its occurrence and also to determine the potential outcomes resulted from the risk occurrence. To do this, fault tree and event tree analysis (ETA) methods are applied. As the application of the traditional approach of these two methods is difficult in many cases due to limited access to information, fuzzy arithmetic can be considered as a useful tool. In this research, first, fault tree structure is created according to consequences resulted from the Delphi method. Then, the probability of risk occurrence is calculated by applying fault tree analysis (FTA) based on fuzzy logic. By establishing the structure of fault tree related to the failure risk of mitigation strategies, the main causes relating to failure of strategies are identified. The structure of the event tree is created using the obtained results; moreover, the expected monetary value (EMV) of risk event is computed. Finally, to validate the results obtained, a model is created by Monte Carlo simulation and then the results obtained by applying the two methods are compared. The EMV of the risk event evaluated in this paper is determined to be 9.93% of the project baseline cost.

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