Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification

• URL: http://arxiv.org/abs/2604.27807v2
• Date: Fri, 01 May 2026 12:54:47 GMT
• Title: Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification
• Abstract summary: The design of embedded safety-critical systems is increasingly challenged by escalating system complexity.<n>Traditional methods for reliability, safety, and security management often fall short in addressing the dynamic and uncertain behaviors introduced by Artificial Intelligence (AI) and Machine Learning (ML)<n>While AI and ML offer powerful predictive, adaptive, and self-optimizing capabilities that can enhance system dependability, their inherent non-determinism, data-dependence, and lack of formal guarantees introduce new challenges for verification, validation, and certification.<n>This paper explores emerging methodologies, architectures, and frameworks for designing dependable autonomous and embedded systems in the era of AI
• Score: 4.358009861529629
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The design of embedded safety-critical systems such as those used in next-generation automotive and autonomous platforms, is increasingly challenged by escalating system complexity, hardware-software heterogeneity, and the integration of intelligent, data-driven components. Ensuring dependability in such systems requires a holistic approach that spans multiple abstraction layers and encompasses both design- and run-time assurance. Traditional methods for reliability, safety, and security management often fall short in addressing the dynamic and uncertain behaviors introduced by Artificial Intelligence (AI) and Machine Learning (ML) components, especially under stringent real-time, power, and safety constraints. While AI and ML offer powerful predictive, adaptive, and self-optimizing capabilities that can enhance system dependability, their inherent non-determinism, data-dependence, and lack of formal guarantees introduce new challenges for verification, validation, and certification. This paper explores emerging methodologies, architectures, and frameworks for designing dependable autonomous and embedded systems in the era of AI. It highlight advances in reliability modeling, secure system design, and certification approaches that account for imperfect, learning-enabled components, aiming to bridge the gap between AI innovation and certifiable system-level dependability.

Related papers

• AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems [14.280538199149634]
  This vision paper introduces AI Deployment Accountability Engineering (ADAE)<n>ADAE is a proposed AI engineering subdiscipline concerned with establishing measurable, continuous, and actionable accountability for deployed AI systems.<n>We articulate a research agenda built around four interconnected pillars: structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational, and institutional risks.
  arXiv  Detail & Related papers  (2026-09-13T15:18:41Z)
• Physically Unclonable Functions for Secure IoT Authentication and Hardware-Anchored AI Model Integrity [1.9795169518658187]
  This survey reviews and synthesizes existing literature on hardware-rooted trust mechanisms for AI-enabled IoT systems.<n>It examines and compares representative trust anchor mechanisms, including TPM, PUFs, container-aware hardware roots of trust, and software-only security approaches.<n>It shows that PUF-based and hybrid trust anchors offer a promising balance for large-scale, AI-enabled IoT systems.
  arXiv  Detail & Related papers  (2026-04-23T01:06:42Z)
• TAIBOM: Bringing Trustworthiness to AI-Enabled Systems [0.23332469289621785]
  Software Bills of Materials (SBOMs) have become critical for enhancing transparency and traceability.<n>Current frameworks fall short in capturing the unique characteristics of AI systems.<n>We introduce Trusted AI Bill of Materials (TAIBOM) -- a novel framework extending SBOM principles to the AI domain.
  arXiv  Detail & Related papers  (2025-10-02T16:17:07Z)
• Towards provable probabilistic safety for scalable embodied AI systems [79.31011047593492]
  Embodied AI systems are increasingly prevalent across various applications.<n> Ensuring their safety in complex operating environments remains a major challenge.<n>This Perspective offers a pathway toward safer, large-scale adoption of embodied AI systems in safety-critical applications.
  arXiv  Detail & Related papers  (2025-06-05T15:46:25Z)
• AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
  We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
  arXiv  Detail & Related papers  (2025-02-24T02:11:52Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022 [0.0]
  The RAISA workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level.
  arXiv  Detail & Related papers  (2022-02-10T01:15:50Z)
• Systems Challenges for Trustworthy Embodied Systems [0.0]
  A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. It is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in
  arXiv  Detail & Related papers  (2022-01-10T15:52:17Z)
• Statistical Perspectives on Reliability of Artificial Intelligence Systems [6.284088451820049]
  We provide statistical perspectives on the reliability of AI systems. We introduce a so-called SMART statistical framework for AI reliability research. We discuss recent developments in modeling and analysis of AI reliability.
  arXiv  Detail & Related papers  (2021-11-09T20:00:14Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.