A functional safety assessment method for cooperative automotive architecture

Kochanthara, Sangeeth; Rood, Niels; Saberi, Arash Khabbaz; Cleophas, Loek; Dajsuren, Yanja; Brand, Mark van den

doi:10.1016/j.jss.2021.110991

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…The argument patterns provide reusable templates for the types of claims that must be made in a compelling argument. Kochanthara et al (2021) propose a safety assessment method on the systems-of-systems level, i.e., for cooperative driving systems. While the method does not target ML specifically, it discusses the context of platooning with a manually driven lead vehicle is followed by autonomous vehicles -a solution that most likely requires ML-based perception.…”

Section: Related Workmentioning

confidence: 99%

Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System

Borg¹,

Henriksson²,

Socha³

et al. 2022

Preprint

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Integration of Machine Learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guidelines are under development to support the safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and the Assurance of Machine Learning for use in Autonomous Systems (AMLAS) framework. SOTIF and AMLAS provide high-level guidance but the details must be chiseled out for each specific case. We report results from an industry-academia collaboration on safety assurance of SMIRK, an ML-based pedestrian

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Section: Related Workmentioning

confidence: 99%

Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System

Borg¹,

Henriksson²,

Socha³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Kochanthara et al [20] have investigated to check whether the architecture for a single vehicle (a participant system in SoS) meets the functional safety requirements for cooperative driving (an SoS). The authors proposed a method to assess functional safety for cooperative vehicles by combining software architecture domains and safety engineering methods.…”

Section: Safety Analysis For System Of Cyber-physical Systemsmentioning

confidence: 99%

“…Fault Traceab among/betwee [17] [19] [20] [21] SafeSoCPS : Partially addressed; : fully addressed; : not addressed.…”

Section: Ref Composite Safety Analysis For Sosmentioning

confidence: 99%

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SafeSoCPS: A Composite Safety Analysis Approach for System of Cyber-Physical Systems

Ali

Hussain

Hong

2022

Sensors

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The System of Cyber-Physical Systems (SoCPS) comprises several independent Cyber-Physical Systems (CPSs) that interact with each other to achieve a common mission that the individual systems cannot achieve on their own. SoCPS are rapidly gaining attention in various domains, e.g., manufacturing, automotive, avionics, healthcare, transportation, and more. SoCPS are extremely large, complex, and safety-critical. As these systems are safety-critical in nature, it is necessary to provide an adequate safety analysis mechanism for these collaborative SoCPS so that the whole network of these CPSs work safely. This safety mechanism must include composite safety analysis for a network of collaborative CPS as a whole. However, existing safety analysis techniques are not built for analyzing safety for dynamically forming networks of CPS. This paper introduces a composite safety analysis approach called SafeSoCPS to analyze hazards for a network of SoCPS. In SafeSoCPS, we analyze potential hazards for the whole network of CPS and trace the faults among participating systems through a fault propagation graph. We developed a tool called SoCPSTracer to support the SafeSoCPS approach. Human Rescue Robot System—a collaborative system—is taken as a case study to validate our proposed approach. The result shows that the SafeSoCPS approach enables us to identify 18 percent more general faults and 63 percent more interaction-related faults in a network of a SoCPS.

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“…Assessing the functional safety of cyber-physical systems is undoubtedly relevant because new vulnerabilities are constantly identified 1 – 5 . Numerous facts of successful cyber attacks indicate a lack of security of cyber-physical systems of all levels and classes.…”

Section: Introductionmentioning

confidence: 99%

The functional safety assessment of cyber-physical system operation process described by Markov chain

Kovtun

Izonin

Greguš

2022

Sci Rep

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The functional safety assessment is one of the primary tasks both at the design stage and at the stage of operation of critical infrastructure at all levels. The article's main contribution is the information technology of calculating the author's metrics of functional safety for estimating the instance of the model of the cyber-physical system operation. The calculation of metric criteria analytically summarizes the results of expert evaluation of the system in VPR-metrics and the results of statistical processing of information on the system's operation presented in the parametric space Markov model of this process. The advantages of the proposed approach are the following: the need to process orders of magnitude less empirical data to obtain objective estimates of the investigated system; taking into account the configuration scheme and architecture of the security subsystem of the investigated system when calculating the metric; completeness, compactness, and simplicity of interpretation of evaluation results; the ability to assess the achievability of the limit values of the metric criteria based on the model of operation of the investigated system. The paper demonstrates the application of the proposed technology to assess the functional safety of the model of a real cyber-physical system.

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A functional safety assessment method for cooperative automotive architecture

Cited by 12 publications

References 9 publications

Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System

Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System

SafeSoCPS: A Composite Safety Analysis Approach for System of Cyber-Physical Systems

The functional safety assessment of cyber-physical system operation process described by Markov chain

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