safety

This paper addresses the insufficient integration of early-system safety analysis with Model-Based Systems Engineering (MBSE). It comparatively evaluates three functional safety analysis methods—Failure Mode and Effects Analysis (FMEA), Functional Hazard Assessment (FHA), and Fault Feedback and Impact Propagation (FFIP)—and identifies FFIP as superior for detecting emergent behaviors, second-order effects, and fault propagation. Subsequently, it systematically reviews existing MBSE integration practices, categorizing them into four approaches: model transformation, custom algorithm development, built-in toolkits, and manual modeling. The study reveals that current integration efforts are predominantly focused on FMEA, while FHA and FFIP remain in exploratory stages, hindered by the absence of a unified framework and standardized guidelines. To bridge this gap, the paper proposes a novel, full-lifecycle safety analysis integration paradigm aligned with digital engineering transformation—enabling traceable, executable, and evolvable model-driven safety verification.

Modern cyber-physical system (CPS) software must continuously deliver safety-critical functionality, yet existing software factories lack deep integration of safety engineering practices, leading to a disconnect between development and safety assurance. Method: This paper introduces the “Safety Factory” paradigm—a novel approach that systematically integrates formal modeling, automated consistency verification, semantically rich safety models, and CI/CD pipelines. It enables model-based, machine-processable, and continuously evolving safety activities through automated documentation generation, formal verification, and safety-aware build pipelines. Contribution/Results: The framework ensures functional safety while significantly improving safety compliance efficiency. Empirical evaluation demonstrates its capability to support rapid iteration and concurrent safety assurance for complex CPSs, effectively bridging the gap between high-velocity development and stringent safety requirements.

This study addresses safety risks arising from software faults in cyber-physical systems (CPS) for electric bicycles. We propose a simulation-driven functional Failure Mode and Effects Analysis (FMEA) method, leveraging Simulink Fault Analyzer to construct fault models, integrated with expert review and a systematic FMEA process to close the loop among fault modeling, simulation-based analysis, and impact assessment. Experimental evaluation identified 13 real-world faults with 100% model accuracy; among them, five revealed previously unrecognized safety implications, and 38.4% induced anomalous system behavior. The study distills ten reusable engineering practice guidelines, significantly enhancing the effectiveness and practicality of FMEA in industrial-scale CPS. It provides empirical validation and methodological contributions toward the operational deployment of simulation-driven safety analysis.

In response to escalating safety and rights risks posed by general-purpose artificial intelligence (GPAI), this paper proposes the first systematic reporting framework for GPAI incidents. Drawing on a systematic literature review and cross-case analysis of high-stakes domains—including aviation and healthcare—as well as regulatory practices in the U.S. and EU, the study identifies seven core dimensions: policy objectives, reporting entities, incident typologies, reporting modalities (mandatory vs. voluntary), near-miss inclusion, anonymity safeguards, and legal immunity provisions. It critically examines the trade-offs among safety learning, cross-organizational information sharing, and legal interoperability inherent in each mechanism. The resulting framework offers policymakers and researchers an actionable, theory-informed blueprint for designing GPAI incident reporting infrastructure—addressing a critical gap in GPAI risk governance and advancing the institutional foundations for responsible AI development and deployment.

Safety-critical cyber-physical systems (CPS), such as artificial pancreas systems (APS), face urgent challenges in real-time prediction and proactive mitigation of safety hazards caused by malicious attacks or unexpected failures. Method: We propose a tightly integrated, knowledge-guided and data-driven safety engine. It introduces, for the first time, a closed-loop framework unifying joint estimation of short- and long-term system trajectories, causal inference of latent safety hazards, and generation of optimal corrective actions—incorporating domain-specific safety constraint knowledge graphs, context-aware mitigation policy libraries, temporal deep learning models (LSTM/TCN), and optimization-based action planning. Contribution/Results: Evaluated on a real-world APS testbed and clinical datasets, our approach achieves a 92.8% hazard mitigation success rate—improving over pure rule-based or pure data-driven baselines by >76%. It guarantees zero false negatives, maintains low false positive rates, and introduces no new safety risks.

This study addresses the inadequacy of current IT compliance–oriented cybersecurity policies in safeguarding the physical safety of cyber-physical systems, as digital failures often precipitate real-world harm. By coding 292 critical infrastructure policies (2000–2025) and aligning them with the NIST SP 800-160 Vol. 2 resilience lifecycle, the research reveals a significant misalignment between prevailing policy approaches—overreliant on IT control catalogs during resistance and recovery phases—and actual physical risks. The work proposes a modernized “duty of reasonable care” standard centered on hazard-specific traceability, structured assurance cases, and cyber resilience engineering. It identifies three critical disconnects: misaligned delegation of standards, reduction of recovery mechanisms to mere incident reporting, and uneven sectoral adaptability. The study further outlines a viable pathway for federal policy that integrates engineering implementation with targeted incentives.

Current AI incident governance frameworks lack consistency in defining, categorizing, monitoring, and reporting incidents, which constrains the depth and accuracy of post-deployment failure analysis. This study addresses this gap through a systematic literature review and comparative analysis across multiple governance frameworks, thereby identifying and synthesizing key inconsistencies that span existing mechanisms. The work reveals systemic deficiencies in data collection practices, classification logics, and analytical rigor, and elucidates critical misalignments among core governance components. By clarifying these structural disconnects, the research establishes a theoretical foundation and proposes a coordinated pathway toward a unified, standardized framework for AI incident governance.

This study addresses the challenge of effectively monitoring early-stage agent systems, where structural flaws often obscure task-level errors. The authors propose a three-dimensional (quality, suitability, efficiency) and three-granularity (intra-run, inter-run, structural) monitoring and triaging framework tailored for low-maturity agent systems. They introduce a novel system maturity staging model based on the coefficient of variation and monitoring granularity, integrated with a severity classification adapted from FMEA to guide human review. The resulting transferable monitoring architecture supports document-driven, multi-stage workflows, enhanced by a synthetic testbed with controlled error injection. Experimental results demonstrate that structural defects significantly mask task-level signals; 97% of issues can be automatically traced, with only 2% requiring human intervention, and each granularity level precisely identifies its corresponding defect type (coefficients of variation: 0.02, 1.25, and 0.00, respectively).