Design Verification and Validation for Reliable Safety-Critical Autonomous Control Systems

Yan, Rong; Yang, Junjie; Zhu, Di; Huang, Kai

doi:10.1109/iceccs2018.2018.00026

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…To that aim, a standard architecture called AUTOSAR is used in the automotive industry. Within this architecture, software applications can be implemented on several electronic control units (ECUs) [82]. In such a context, reference [82] proposes a verification approach for software systems running on hardware platforms subject to transient faults.…”

Section: H Formal Methodsmentioning

confidence: 99%

“…Within this architecture, software applications can be implemented on several electronic control units (ECUs) [82]. In such a context, reference [82] proposes a verification approach for software systems running on hardware platforms subject to transient faults. To be able to formally verify the model, the AUTOSAR model is converted into timed automata.…”

Section: H Formal Methodsmentioning

confidence: 99%

“…Regarding mitigation of transient faults in AVs, we have mentioned formal approaches for CPSs [82] and fault injection (BinFI [6]). In particular, BinFI does not only cover transient errors but also systematic errors with better error coverage than DeepXplore [8].…”

Section: Open Issues Challenges and Opportunitiesmentioning

confidence: 99%

See 2 more Smart Citations

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

et al. 2021

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Section: H Formal Methodsmentioning

confidence: 99%

Section: H Formal Methodsmentioning

confidence: 99%

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Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

et al. 2021

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“…Some specifically mention some software fault-tolerance measures. The measures include input checker [16], output checker [17,18], replication [19], design diversity [20], and voting [21]. However, in many of these papers, the details of the measures are not described very rigorously.…”

Section: Previous Workmentioning

confidence: 99%

“…If the other ML model is also detected to be erroneous, the input can be passed on to a third one and so on as long as there are more redundant ML models to pass the input on to. Recovery blocks can be set up by replication [11,19] or design diversity [11,20]. In replication, the software components -in our case, the ML model -to which the inputs are passed on are other instances of the initial ML model; in other words, its replicas.…”

Section: Fault-tolerance Solution Proposalsmentioning

confidence: 99%

On Misbehaviour and Fault Tolerance in Machine Learning Systems

Myllyaho¹,

Raatikainen²,

Männistö³

et al. 2021

Preprint

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Machine learning (ML) provides us with numerous opportunities, allowing ML systems to adapt to new situations and contexts. At the same time, this adaptability raises uncertainties concerning the run-time product quality or dependability, such as reliability and security, of these systems. Systems can be tested and monitored, but this does not provide protection against faults and failures in adapted ML systems themselves. We studied software designs that aim at introducing fault tolerance in ML systems so that possible problems in ML components of the systems can be avoided. The research was conducted as a case study, and its data was collected through five semi-structured interviews with experienced software architects. We present a conceptualisation of the misbehaviour of ML systems, the perceived role of fault tolerance, and the designs used. Common patterns to incorporating ML components in design in a fault tolerant fashion have started to emerge. ML models are, for example, guarded by monitoring the inputs and their distribution, and enforcing business rules on acceptable outputs. Multiple, specialised ML models are used to adapt to the variations and changes in the surrounding world, and simpler fall-over techniques like default outputs are put in place to have systems up and running in the face of problems. However, the general role of these patterns is not widely acknowledged. This is mainly due to the relative immaturity of using ML as part of a complete software system: the field still lacks established frameworks and practices beyond training to implement, operate, and maintain the software that utilises ML. ML software engineering needs further analysis and development on all fronts.

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Validation Framework of a Digital Twin: A System Identification Approach

Phillips,

Kenley

2024

INCOSE International Symp

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The constant improvement and developments in Artificial Intelligence/Machine learning models coupled with increased computing power have led to the incorporation of AI/ML for simulating learning and problem‐solving in simple and complex engineering systems. This latent uncertainty and unpredictable characteristics of AI‐enabled systems challenges engineers and industry stakeholders who care about ensuring the right systems are built (system validation). Digital Twins are an excellent example of such AI‐enabled systems due to their data‐dependent nature when tasked with replicating, monitoring, and updating physical assets for structural health monitoring and control. However, Digital Twins' system validation has not been well‐researched. This study delves into existing research and frameworks for validating Digital Twins and proposes a novel model‐centric validation framework based on system identification techniques. As a case study, we apply this model‐centric validation framework towards partially validating a Digital Twin for a single‐heat‐pipe test article for a Microre‐actor Agile Non‐nuclear Experimental Testbed.

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Design Verification and Validation for Reliable Safety-Critical Autonomous Control Systems

Cited by 5 publications

References 20 publications

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

On Misbehaviour and Fault Tolerance in Machine Learning Systems

Validation Framework of a Digital Twin: A System Identification Approach

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