A Computationally Efficient Learning of Artificial Intelligence System Reliability Considering Error Propagation

2026-03-18

AI Summary

This work addresses the reliability challenges of AI systems in smart cities, which are often hindered by error propagation across processing stages, data scarcity, and high computational complexity. To this end, we propose the first reliability modeling framework that integrates the mechanisms of error propagation with efficient learning algorithms. Leveraging a physics-driven autonomous driving simulation platform and a controllable error injection mechanism, we generate high-quality data to construct a sequential reliability model that explicitly captures inter-stage error dependencies. A composite likelihood-based EM algorithm is designed for efficient parameter estimation, with theoretical guarantees on convergence. Experimental results demonstrate that the proposed method significantly improves prediction accuracy while reducing computational overhead in autonomous driving perception systems.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• AI system reliability
• error propagation
• computational complexity
• data scarcity
• interdependent errors

Innovation

• error propagation
• AI system reliability
• composite likelihood EM
• autonomous vehicle simulation
• computationally efficient modeling