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.

翻译：嵌入式安全关键系统（如下一代汽车和自主平台中使用的系统）的设计正面临日益严峻的挑战，这些挑战源于系统复杂性提升、软硬件异构性以及智能数据驱动组件的集成。要确保此类系统的可信性，需要一种涵盖多个抽象层次并融合设计时与运行时保障的整体性方法。传统的可靠性、安全性与保障性管理方法往往难以应对人工智能（AI）和机器学习（ML）组件引入的动态与不确定性行为，尤其是在严苛的实时性、功耗和安全性约束下。尽管AI和ML具备强大的预测、自适应和自优化能力，可提升系统可信性，但其固有的非确定性、数据依赖性以及缺乏形式化保证的特性，为验证、确认和认证带来了新挑战。本文探讨了AI时代下可信自主与嵌入式系统设计中的新兴方法、架构与框架，重点阐述了考虑非完美、学习型组件的可靠性建模、安全系统设计及认证方法的最新进展，旨在弥合AI创新与可认证系统级可信性之间的鸿沟。