Artificial Intelligence (AI) systems are increasingly prominent in emerging smart cities, yet their reliability remains a critical concern. These systems typically operate through a sequence of interconnected functional stages, where upstream errors may propagate to downstream stages, ultimately affecting overall system reliability. Quantifying such error propagation is essential for accurate modeling of AI system reliability. However, this task is challenging due to: i) data availability: real-world AI system reliability data are often scarce and constrained by privacy concerns; ii) model validity: recurring error events across sequential stages are interdependent, violating the independence assumptions of statistical inference; and iii) computational complexity: AI systems process large volumes of high-speed data, resulting in frequent and complex recurrent error events that are difficult to track and analyze. To address these challenges, this paper leverages a physics-based autonomous vehicle simulation platform with a justifiable error injector to generate high-quality data for AI system reliability analysis. Building on this data, a new reliability modeling framework is developed to explicitly characterize error propagation across stages. Model parameters are estimated using a computationally efficient, theoretically guaranteed composite likelihood expectation - maximization algorithm. Its application to the reliability modeling for autonomous vehicle perception systems demonstrates its predictive accuracy and computational efficiency.

翻译：人工智能（AI）系统在新兴智慧城市中的应用日益广泛，但其可靠性仍是关键问题。此类系统通常通过一系列相互连接的功能阶段运行，其中上游误差可能传播至下游阶段，最终影响整体系统可靠性。准确量化此类误差传播对于建立精确的AI系统可靠性模型至关重要。然而，该任务面临以下挑战：i）数据可用性：真实世界的AI系统可靠性数据往往稀缺且受隐私问题制约；ii）模型有效性：跨序贯阶段重复出现的误差事件具有相互依赖性，违反了统计推断的独立性假设；iii）计算复杂性：AI系统处理大量高速数据，导致频繁且复杂的重复性误差事件难以追踪与分析。为应对这些挑战，本文利用基于物理的自动驾驶仿真平台与可证明合理的误差注入器生成高质量数据，用于AI系统可靠性分析。在此数据基础上，开发了一种新的可靠性建模框架，以显式刻画跨阶段的误差传播。模型参数采用计算高效且具有理论保证的复合似然期望最大化算法进行估计。该算法在自动驾驶感知系统可靠性建模中的应用验证了其预测精度与计算效率。