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Surveying Uncertainty Representation: A Unified Model for Cyber-Physical Systems

Johannes Mäkelburg, Diego Perez-Palacin, Raffaela Mirandola, Maribel Acosta

TL;DR

This work provides a comprehensive survey of uncertainty representations in cyber-physical systems and identifies core gaps in terminology, CPS component differentiation, and autonomy handling. It proposes a harmonized Conceptual Model of Uncertainty Representations in CPS that extends the PSUM Metamodel with CPS-specific categories, including Uncertainty Kind, Reducibility, Nature, Effect, Perspective, Pattern, Level, Risk, Location, and Source, linked to Atomic Components and Systems via a Composite pattern. The model is demonstrated in an automotive context, illustrating how uncertainties can be localized, classified, and mitigated across cyber, physical, and platform components in run-time scenarios. The paper highlights practical implications for design-time and run-time uncertainty management, and outlines future directions for standardized terminology, quantified uncertainty propagation, and human-system collaboration in CPS.

Abstract

Cyber-Physical Systems (CPS) operate in dynamic environments, leading to different types of uncertainty. This work provides a comprehensive review of uncertainty representations and categorizes them based on the dimensions used to represent uncertainty. Through this categorization, key gaps and limitations in existing approaches are identified. To address these issues, a Conceptual Model of Uncertainty Representations in CPS is introduced, integrating and extending existing models. Its applicability is demonstrated through examples from the automotive domain, showing its effectiveness in capturing and structuring uncertainty in real-world scenarios.

Surveying Uncertainty Representation: A Unified Model for Cyber-Physical Systems

TL;DR

This work provides a comprehensive survey of uncertainty representations in cyber-physical systems and identifies core gaps in terminology, CPS component differentiation, and autonomy handling. It proposes a harmonized Conceptual Model of Uncertainty Representations in CPS that extends the PSUM Metamodel with CPS-specific categories, including Uncertainty Kind, Reducibility, Nature, Effect, Perspective, Pattern, Level, Risk, Location, and Source, linked to Atomic Components and Systems via a Composite pattern. The model is demonstrated in an automotive context, illustrating how uncertainties can be localized, classified, and mitigated across cyber, physical, and platform components in run-time scenarios. The paper highlights practical implications for design-time and run-time uncertainty management, and outlines future directions for standardized terminology, quantified uncertainty propagation, and human-system collaboration in CPS.

Abstract

Cyber-Physical Systems (CPS) operate in dynamic environments, leading to different types of uncertainty. This work provides a comprehensive review of uncertainty representations and categorizes them based on the dimensions used to represent uncertainty. Through this categorization, key gaps and limitations in existing approaches are identified. To address these issues, a Conceptual Model of Uncertainty Representations in CPS is introduced, integrating and extending existing models. Its applicability is demonstrated through examples from the automotive domain, showing its effectiveness in capturing and structuring uncertainty in real-world scenarios.

Paper Structure

This paper contains 39 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Uncertainty Surveys
  3. Contributions
  4. Paper Structure
  5. Overview of the related Literature
  6. Location
  7. Source
  8. Uncertainty Types
  9. Time
  10. Level
  11. Reducibility
  12. Nature
  13. Type of Model
  14. Pattern
  15. Uncertainty Effect
  16. ...and 24 more sections

Figures (7)

  • Figure 1: Overview of different categories representing Uncertainty across various frameworks.
  • Figure 2: Overview of terms describing the Location of Uncertainty across various frameworks. Colors indicate terms with identical names but differing meanings across frameworks.
  • Figure 3: Harmonized and Extended Conceptual Uncertainty Representation Model.
  • Figure 4: Overview of terms describing the Location of Uncertainty across various frameworks. Colors indicate terms with identical names but differing meanings across frameworks, while orange highlights the introduced consistent terminology.
  • Figure 5: Model of a selection of components in the autonomous vehicle
  • ...and 2 more figures