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的系统）的设计，正日益受到系统复杂度攀升、软硬件异构性增强以及智能化数据驱动组件集成的挑战。确保此类系统的可信性，需要一种贯穿多个抽象层、涵盖设计阶段与运行时保障的整体性方法。传统的可靠性、安全性与安全管理方法，往往难以应对人工智能（AI）与机器学习（ML）组件所引入的动态性和不确定性行为，尤其是在严格的实时、功耗与安全约束条件下。尽管AI与ML提供了强大的预测、自适应及自优化能力以增强系统可信性，但其固有的非确定性、数据依赖性及形式化保证的缺失，给验证、确认与认证带来了新挑战。本文探索了AI时代下设计可信自主与嵌入式系统的新兴方法、架构及框架，重点阐述了针对含不完美学习组件的系统在可靠性建模、安全设计与认证方法方面的进展，旨在弥合AI创新与可认证级系统可信性之间的鸿沟。