Browse Topic: Safety critical systems
1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential relationships and influences during the design phase. The novelty of this framework comes from providing a holistic evaluation that captures testing of interactions between modules in addition to component-level testing, while other frameworks focus on testing individual component behaviors. It also assesses the equality of two interactions, meaning it ensures that two interactions behave the same way for consistent results. Moreover, the framework serves as a valuable tool for AV designers and safety regulators to aid in establishing robust design and assessment approaches. This work highlights the need for a common framework to thoroughly test AVs and gain a holistic understanding of system interactions. Finally, the framework aims to understand how to mitigate potential influences leading to AV malfunctions to advance the development and deployment of safe and reliable Automated Vehicles. The work focuses on level 1 and level 4 automated driving features to simplify the work, although it can be from level 1 to level 5. Although framework performance is inherently difficult to quantify, this framework’s performance can be reflected through its ability to accurately capture system interactions for improved AV design and support a broader usability among AV stakeholders. In the future, the framework can be expanded to include additional elements, such as infrastructure or other vehicles, to analyze information provided to AVs, allowing experts from various domains to collaborate, create similar models, integrate them when feasible, and model the interactions in real-time.
Why ADAS validation can't be solved with more miles alone. Modern advanced driver assistance systems (ADAS) are expected to operate reliably across an almost limitless range of real-world conditions, including changing weather, low lighting, unpredictable traffic behavior, and sensor noise. Validating performance across that level of variability has become one of the most demanding parts of ADAS development. Physical road testing, or even large-scale simulation, alone cannot provide sufficient coverage to meet these demands. This challenge is driven by increasing system complexity. Modern ADAS platforms rely on machine-learning-based perception, multi-sensor fusion, and tightly integrated software architectures that must interpret complex sensor data in real time. Each additional sensing modality, software update, or feature expansion drives a significant validation effort and, in many cases, increases the number of scenarios that must be evaluated.
Developing high-integrity software is a complex process that involves meeting strict standards across various industries. For instance, in the avionics sector, the DO-178C Design Assurance Level A (DAL-A) sets the highest level of rigor, requiring comprehensive evidence that the software will perform its intended safety functions. Modern avionics systems are made up of hardware and software from different vendors, all integrated by prime contractors. By achieving modularity in these systems, we can reduce interface complexity, manage version control, address supply chain vulnerabilities, and significantly lower recertification costs. To support a high degree of integration and software reuse in avionics systems, certain architectural elements are necessary. These include a certified Real-Time Operating System (RTOS), open standards consortia like FACE® and MOSA, multicore partitioning strategies, deterministic networking, and hypervisor-based virtualization. The role of a certified RTOS, for example, is crucial in ensuring the reliable and efficient operation of safety-critical software components. Open standards consortia, on the other hand, facilitate the development of interoperable systems, while multicore partitioning strategies enable the efficient use of system resources. The use of deterministic networking and hypervisor-based virtualization also plays a key role in enabling the integration of multiple systems and reducing the complexity of system design. By leveraging these technologies, we can create a 'certify once, deploy anywhere' paradigm, which reduces development timelines, lowers lifecycle costs, and positions safety-critical software components for reuse across heterogeneous platforms. This approach not only improves the efficiency of system development but also enhances the reliability and safety of the resulting systems. In essence, the development of high-integrity software for avionics systems requires a comprehensive approach that considers the complex interactions between hardware and software components. By adopting modular architectures and leveraging open standards, certified RTOS, and advanced networking and virtualization technologies, we can create systems that are not only safe and reliable but also efficient and cost-effective. This, in turn, can help reduce the risks associated with system development and deployment, while also improving the overall performance and safety of the resulting systems.
This paper introduces a robust supervised machine learning framework for estimating helicopter gross weight during the takeoff phase. The methodology leverages high-fidelity datasets from Airbus's global in-service fleet to ensure a reliable training foundation. At the core of the approach is a long short-term memory recurrent neural network, supported by a patented data-curation pipeline designed to maintain high data integrity. To align with rigorous aviation safety standards, the study outlines a learning assurance process compliant with EASA guidelines, specifically addressing safety assessment objectives for machine learning. A central innovation is the characterization and monitoring of the model's operational design domain through multidimensional functional principal component analysis. By projecting high-dimensional, non-linear sensor data into a manageable tabular subspace, this approach enables the definition of safety envelopes using explainable and efficient classical methods. Validated against diverse real-world flight profiles, the framework demonstrates high predictive accuracy, marking a significant milestone toward deploying the model on airborne targets for safety-critical functions such as condition-based maintenance.
Software is driving major changes in automotive design. The rise of the software-defined vehicle, combined with increasing automation, is dramatically increasing software complexity. Automotive teams must deliver larger volumes of safety-critical code on tighter schedules while maintaining strict compliance with functional safety standards. In this environment, effective testing and verification are more important than ever. Development teams are increasingly adopting shift-left testing strategies, where defects are identified early at the unit level before software progresses down the development pipeline. Detecting issues earlier reduces risk, lowers remediation costs, and improves development velocity.
Patching vulnerabilities in safety-critical domains such as automotive and aerospace is costly and complex. A small code modification can trigger a complete rebuild, producing a binary with widespread changes. This inflates patch size, complicates regression testing, and makes over-the-air (OTA) updates inefficient, as traditional binary patches often replace large portions of the executable. We present a binary rewriting–based experiment that shows the feasibility of a patch that updates only the affected bytes by computing the impact of a code change at the binary level. This produces minimal, localized patches rather than regenerated executables. The preliminary experiment shows that a single source change, which leads to thousands of modified bytes after recompilation, can be captured with only a few bytes using our method. For automotive and aerospace systems, this technique reduces patch size, conserves bandwidth, and minimizes disruption to certified software, offering a promising direction for efficient and reliable vulnerability remediation.
Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software boundaries. Evaluations demonstrate a latency reduction from a baseline of 45.3 milliseconds to an optimized 23.5 milliseconds, representing a 48.1% improvement without sacrificing spatial resolution or detection robustness. Under optimized configurations, the framework sustains frame rates above 60 FPS at both Full HD and 4K resolutions, with frame drop rates held to approximately 3%. Perceptual evaluation further confirms that object detection accuracy consistently exceeds 91% within the <35 ms latency range, while collision-prediction delays are reduced to below 12.4 ms, ensuring timely responses in dynamic scenarios. These improvements collectively validate the critical importance of hardware-software co-design for embedded vision systems. The results highlight that ultra-low-latency perception is achievable on edge platforms when pipelines are designed with cross-layer optimization, bridging sensor interfaces, video codecs, network transport, and GPU computation. The proposed architecture provides a scalable foundation for future embedded vision deployments in autonomous driving, robotics, and unmanned aerial systems, where low latency is a non-negotiable requirement for safety, reliability, and operational efficiency.
This paper presents the first systematic examination of Large Language Model (LLM) capabilities for automating the development of Failure Mode and Effects Analysis (FMEA) utilizing architectural diagrams as input. Although prior research has examined LLMs for FMEA tasks, our methodology incorporates innovative aspects, such as the direct analysis of architectural diagrams for component extraction, prediction of failure modes, causes, estimation of risk and a human-in-the-loop (Hu-IL) validation framework. We examine the capability of general-purpose LLMs to accurately automate the creation of FMEA by formulating a methodology that extracts components and signals from architectural diagrams, conducts automated component classification, and produces a comprehensive FMEA form sheet encompassing Severity, Occurrence, and Detectability (S/O/D) scoring. Our methodology is grounded in structured prompt engineering theory, utilizing scope bounding techniques to reduce hallucination while preserving extraction accuracy. Assessment against expert-validated ground truth (over 12 years of functional safety experience) across several automotive system diagrams indicates a 92% accuracy rate for signal extraction and component categorization, with S/O/D scoring obtaining an accuracy range of 70–90%. The results demonstrate substantial potential to reduce manual FMEA development processes (as compared to prior studies). Key limitations include sensitivity to diagram complexity and quality, as inadequately designed diagrams markedly affect output precision along with the inability of LLMs to create new detection measures reliably. Our Hu-IL validation process mitigates these limitations while preserving the advantages of automation. This study provides baseline performance indicators for LLM-based FMEA automation and demonstrates significant potential in transforming traditional FMEA workflows in safety-critical industries.
Military and aerospace applications have become increasingly complex real-time systems. Multi-core SoCs improve performance but create new challenges in maintaining and verifying deterministic behavior. Connected systems require exceptional security to protect code from external cyberattacks. Evolving functional safety and reliability standards that keep raising the bar mean developers need to begin comprehensive testing sooner if they are going to meet tighter design schedules. Finally, certifying these complex systems has become even more difficult. To help OEMs meet these challenges, the RISC-V architecture has been designed with unique capabilities that support reliability and security in the development of safety-critical applications. With its open instruction set architecture, modularity, and extensibility, RISC-V accelerates the design of functionally safe systems while reducing the complexity, cost, and risk associated with certification to standards like DO-178C and ISO 26262.
Treat foundational AV safety like seatbelts - make it non-proprietary and universal. An open safety stack, shared scenarios, benchmarks, and core validation tools can speed certification, reduce duplicated V&V and build public trust while preserving vendor differentiation. The bottleneck isn't compute - it's verification. Autonomous features are shipping in more vehicles and markets, but the gating factor is no longer raw compute. It's whether developers and regulators can verify systems against requirements and validate them against real-world operating design domains (ODDs) with confidence and repeatability. Today, many safety-critical components, from scenario libraries to pass/fail criteria, live in proprietary silos. That fragmentation slows regression testing, complicates regulator audits across regions, and duplicates effort across the industry. The result is an expensive, bespoke path to certification for every program and geography.
The rapid evolution of modern automotive systems—powered by advancements in autonomous driving and connected vehicle technologies— pose fundamental challenges to design and integration. A specific challenge of these highly interconnected, software-driven systems is in ensuring their safety while avoiding spiralling costs and development times. This challenge calls for a more structured and rigorous approach to safety assurance than traditional methods. Traditional safety cases tend to take a linear, justification-focused approach that mainly focuses on positive assertions —compliance to safety —while giving limited attention to potential weaknesses, or gaps in supporting evidence. This practice may lead to criticism that such arguments are “too positive,” portraying an overly biased or optimistic view of system safety without sufficiently acknowledging areas of unresolved risk. As a result, conventional approaches for developing a safety case may overlook complex interactions, assumptions, and uncertainties that require critical examination, not default acceptance. As opposed to traditional methods of developing safety cases through justification, the dialectic approach emphasizes critical analysis and scrutiny of weak points using open challenges, counterarguments, and alternative perspectives. It encourages a deliberate effort to explore not just what works in a design, but what might fail— anticipating negative aspects, design vulnerabilities, and areas where safety assumptions may fail. Rather than simply validating assumptions, it aims to uncover hidden flaws, inconsistencies, and evidence gaps that could compromise system safety. Constructing a safety case early in a project, and allowing constructive criticism through dialectic argument, transforms the safety case into a living, questioning tool that evolves with improving system understanding—becoming increasingly transparent, robust, and credible. In the paper, we demonstrate 3SK’s practical application of a dialectic methodology for developing safety cases. By this approach, we were able to pick out important safety gaps that would otherwise have gone unnoticed, hence enhancing the completeness, credibility, and robustness of our safety assurance practices.
As vehicles are becoming more complex, maintaining the effectiveness of safety critical systems like adaptive cruise control, lane keep assist, electronic breaking and airbag deployment extends far beyond the initial design and manufacturing. In the automotive industry these safety systems must perform reliably over the years under varying environmental conditions. This paper examines the critical role of periodic maintenance in sustaining the long-term safety and functional integrity of these systems throughout the lifecycle. As per the latest data from the Ministry of Road Transport and Highways (MoRTH), in 2022, India reported a total of 4.61 lakh road accidents, resulting in 1.68 lakh fatalities and 4.43 lakh injuries. The number of fatalities could have been reduced by the intervention of periodic services and monitoring the health of safety critical systems. While periodic maintenance has contributed to long term safety of the vehicles, there are a lot of vehicles on the road which are not serviced regularly. This paper aims to fill this critical gap by proposing a system where the government agencies actively collect and monitor vehicle maintenance data and diagnostics data ensuring that all vehicles on the road undergo mandatory periodic servicing to uphold the integrity of safety-critical systems. This paper concludes by proposing a centralized framework for data sharing and proactive monitoring to ensure the sustained performance of safety-critical systems—ultimately reducing preventable road fatalities and improving overall vehicular safety across India.
Direct current (DC) systems are increasingly used in small power system applications ranging from combined heat and power plants aided with photovoltaic (PV) installations to powertrains of small electric vehicles. A critical safety issue in these systems is the occurrence of series arc faults, which can lead to fires due to high temperatures. This paper presents a model-based method for detecting such faults in medium- and high-voltage DC circuits. Unlike traditional approaches that rely on high-frequency signal analysis, the proposed method uses a physical circuit model and a high-gain observer to estimate deviations from nominal operation. The detection criterion is based on the variance of a disturbance estimate, allowing fast and reliable fault identification. Experimental validation is conducted using a PV system with an arc generator to simulate faults. The results demonstrate the effectiveness of the method in distinguishing fault events from normal operating variations. The method is compared with a recursive least squares estimator, showing improved performance in terms of sensitivity. The approach offers a cost-effective and robust solution to improve safety in DC power systems, particularly in PV and electric mobility applications.
Known as FOSS (for fiber optic sensing system), NASA’s patented, award-winning technology portfolio combines advanced sensors and innovative algorithms into a robust package that accurately and cost-effectively monitors a host of critical parameters in real time. These include position/deformation (displacement, twist, rotation), stiffness (bending, torsion, vibration), operational loads (bending moments, shear loads, torques), strength/stress (pressure/fatigue, breakage prediction), and magnetic fields (cracks or other flaws in safety-critical metal structures) for structural health monitoring applications. In addition to monitoring the structure of a tank, FOSS is capable of sensing the tank’s inventory, including amounts, temperatures, and stratification.
The development of cyber-physical systems necessarily involves the expertise of an interdisciplinary team – not all of whom have deep embedded software knowledge. Graphical software development environments alleviate many of these challenges but in turn create concerns for their appropriateness in a rigorous software initiative. Their tool suites further enable the creation of physics models which can be coupled in the loop with the corresponding software component’s control law in an integrated test environment. Such a methodology addresses many of the challenges that arise in trying to create suitable test cases for physics-based problems. If the test developer ensures that test development in such a methodology observes software engineering’s design-for-change paradigm, the test harness can be reused from a virtualized environment to one using a hardware-in-the-loop simulator and/or production machinery. Concerns over the lack of model-based software engineering’s rigor can be mitigated at each point in the development cycle – setting the stage for the methodology’s power in safety-critical software systems; it is an approach that is proven in use at aerospace companies flying rockets and some leading-edge automotive companies.
Advanced motion control technologies are essential to modern aerospace design, supporting a wide range of safety-critical and comfort-driven applications. In aerospace, motion control components such as gas springs, actuators, and dampers are integral to nearly every commercial aircraft, rocket, satellite, and space vehicle. These critical elements support flight safety and transport functions, from the dependable deployment of landing gear and cargo doors to the smooth, ergonomic operation of seating for pilots and passengers.
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