Related papers: The CLEARSY Safety Platform: 5 Years of Research, …
Developing safety critical applications often require rare human resources to complete successfully while off-the-shelf block solutions appear difficult to adapt especially during short-term projects. The CLEARSY Safety Platform fulfils a…
Traditional automation technologies alone are not sufficient to enable driverless operation of trains (called Grade of Automation (GoA) 4) on non-restricted infrastructure. The required perception tasks are nowadays realized using Machine…
The Scalable Systems Laboratory (SSL), part of the IRIS-HEP Software Institute, provides Institute participants and HEP software developers generally with a means to transition their R&D from conceptual toys to testbeds to production-scale…
In recent years, curial incidents and accidents have been reported due to un-intended control caused by misjudgment of statistical machine learning (SML), which include deep learning. The international functional safety standards for…
Synthesizing controllers for large, complex, and distributed systems is a challenging task. Numerous proposed methods exist in the literature, but it is difficult for practitioners to apply them -- most proposed synthesis methods lack…
Networked control systems (NCS) are widely used in safety-critical applications, but they are often analyzed under the assumption of ideal communication channels. This work focuses on the synthesis of safety controllers for discrete-time…
Automotive companies increasingly adopt scaled agile methods to allow them to deal with their organisational and product complexity. Suitable methods are needed to ensure safety when developing automotive systems. On a small scale, R-Scrum…
Implementing software security practices is a critical concern in modern software development. Industry practitioners, security tool providers, and researchers have provided standard security guidelines and sophisticated security…
DevSecOps, as the extension of DevOps with security training and tools, has become a popular way of developing modern software, especially in the Internet of Things arena, due to its focus on rapid development, with short release cycles,…
Agile techniques recently have received attention in developing safety-critical systems. However, a lack of empirical knowledge of performing safety assurance techniques in practice, especially safety analysis into agile development…
The onward development of information and communication technology has led to a new industrial revolution called Industry 4.0. This revolution involves Cyber-Physical Production Systems (CPPS), which consist of intelligent Cyber-Physical…
Security engineering, from security requirements engineering to the implementation of cryptographic protocols, is often supported by domain-specific languages (DSLs). Unfortunately, a lack of knowledge about these DSLs, such as which…
Domain-Specific Languages (DSLs) help practitioners in contributing solutions to challenges of specific domains. The efficient development of user-friendly DSLs suitable for industrial practitioners with little expertise in modelling still…
We present the SCenario Specification Language (SCSL) for automated generation and execution of system-level tests. SCSL targets complex distributed systems (e.g., collaborating autonomous robots) where classical model-based testing becomes…
Designing Reinforcement Learning (RL) solutions for real-life problems remains a significant challenge. A major area of concern is safety. "Shielding" is a popular technique to enforce safety in RL by turning user-defined safety…
Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aeronautic has proven to be very challenging, since the shift…
We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for defining verification problems by using WhyML, the mature and…
Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary…
Responsibility-sensitive safety (RSS) is an approach to the safety of automated driving systems (ADS). It aims to introduce mathematically formulated safety rules, compliance with which guarantees collision avoidance as a mathematical…
Construction safety research is a critical field in civil engineering, aiming to mitigate risks and prevent injuries through the analysis of site conditions and human factors. However, the limited volume and lack of diversity in existing…