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

• URL: http://arxiv.org/abs/2603.18201v1
• Date: Wed, 18 Mar 2026 18:53:46 GMT
• Title: A Computationally Efficient Learning of Artificial Intelligence System Reliability Considering Error Propagation
• Abstract summary: This paper uses a physics-based autonomous vehicle simulation platform to generate high-quality data for AI system reliability analysis.<n>A new reliability modeling framework is developed to explicitly characterize error propagation across stages.<n>Its application to the reliability modeling for autonomous vehicle perception systems demonstrates its predictive accuracy and computational efficiency.
• Score: 4.088049786040034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Representative Dataset Generation Framework for AI-based Failure Analysis during real-time Validation of Automotive Software Systems [2.6126272668390373]
  A novel approach based on Hardware-in-the-Loop (HIL) simulation and real-time Fault Injection (FI) method is proposed.<n>The framework can generate not only sequential data, but also textual data including fault logs.
  arXiv  Detail & Related papers  (2026-08-10T14:28:42Z)
• UModel: An Agent-Ready Observability Data Modeling Method at Scale [25.076726153200084]
  We present UModel, a unified ontological framework that shifts observability from data-centric to object-centric modeling.<n>By re-modeling the "AIOps 2025 Challenge" dataset using UModel, the precision of root cause localization improved by 8%.<n>UModel has served tens of thousands of users, sustained millions of operations per second, and delivered sub-second query latency.
  arXiv  Detail & Related papers  (2026-06-03T12:25:50Z)
• An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation [2.7082111912355877]
  An intelligent model was developed to analyze the recordings from the real-time validation process of ASSs.<n>The employment of explainable AI techniques, i.e., IGs, DeepLIFT, Gradient SHAP, and DeepLIFT SHAP, was instrumental in enabling model adaptation and facilitating the root cause analysis (RCA)
  arXiv  Detail & Related papers  (2026-03-09T09:46:28Z)
• Out-of-Distribution Detection for Continual Learning: Design Principles and Benchmarking [44.75780122845172]
  Recent years have witnessed significant progress in the development of machine learning models across a wide range of fields.<n>As these models are deployed in ever-changing real-world scenarios, their ability to remain reliable and adaptive over time becomes increasingly important.
  arXiv  Detail & Related papers  (2025-12-16T22:50:01Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Control Analysis and Design for Autonomous Vehicles Subject to Imperfect AI-Based Perception [12.672967565682724]
  Safety is a critical concern in autonomous vehicle (AV) systems, especially when AI-based sensing and perception modules are involved.<n>This paper aims to develop new modeling, analysis, and synthesis tools for AI-based AVs.
  arXiv  Detail & Related papers  (2025-09-15T17:03:21Z)
• SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields [11.872753517172555]
  We present SCENT, a novel framework for scalable and continuity-informed modeling learning.<n>SCENT unifies representation, reconstruction, and forecasting within a single architecture.<n>We validate SCENT through extensive simulations and real-world experiments, demonstrating state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-04-16T17:17:31Z)
• A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops [55.07063067759609]
  High-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted.<n>Models increasingly generate their own data for further training, forming Self-consuming Training Loops (STLs)<n>Some models degrade or even collapse, while others successfully avoid these failures, leaving a significant gap in theoretical understanding.
  arXiv  Detail & Related papers  (2025-02-26T06:18:13Z)
• MIBP-Cert: Certified Training against Data Perturbations with Mixed-Integer Bilinear Programs [50.41998220099097]
  Data errors, corruptions, and poisoning attacks during training pose a major threat to the reliability of modern AI systems.<n>We introduce MIBP-Cert, a novel certification method based on mixed-integer bilinear programming (MIBP)<n>By computing the set of parameters reachable through perturbed or manipulated data, we can predict all possible outcomes and guarantee robustness.
  arXiv  Detail & Related papers  (2024-12-13T14:56:39Z)
• Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning [0.0]
  We present a timeseries-aware uncertainty wrapper for dependable uncertainty estimates on timeseries data. We show that it is possible to increase model accuracy through information fusion and additionally increase the quality of uncertainty estimates.
  arXiv  Detail & Related papers  (2023-05-24T08:24:54Z)
• AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios [51.94807626839365]
  We propose the attention-inspired numerical solver (AttNS) to solve differential equations due to limited data.<n>AttNS is inspired by the effectiveness of attention modules in Residual Neural Networks (ResNet) in enhancing model generalization and robustness.
  arXiv  Detail & Related papers  (2023-02-05T01:39:21Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.