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

2026-04-30

AI Summary

This work addresses the challenges posed by integrating AI components into intelligent autonomous systems—namely, non-determinism, data dependency, and the absence of formal guarantees—which render traditional safety and certification approaches inadequate in complex, heterogeneous environments characterized by dynamic uncertainty. To overcome these limitations, the study proposes a holistic, cross-layer methodology spanning both design and runtime phases, integrating reliability modeling, safety-aware architectures, runtime assurance mechanisms, and novel certification strategies tailored to the unique characteristics of learning-enabled components. By transcending conventional siloed analysis paradigms, this approach establishes a unified trustworthy design framework specifically for AI-driven systems. The research bridges the gap between AI innovation and system-level certification, offering a certifiable design pathway for uncertain, data-driven embedded autonomous systems and significantly enhancing their overall trustworthiness under stringent constraints of real-time performance, power consumption, and safety.

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.

Problem

• Autonomous Systems
• Dependability
• AI Integration
• Safety-Critical Systems
• Certification

Innovation

• dependability
• AI-enabled systems
• certification
• reliability modeling
• secure system design