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Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems

Authors Lorenz Klampfl , Franz Wotawa

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Author Details

Lorenz Klampfl
  • CD Laboratory QAMCAS, Institute of Software Technology, Graz University of Technology, Austria
Franz Wotawa
  • Institute of Software Technology, Graz University of Technology, Austria

Funding

The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology, and Development, and the Christian Doppler Research Association is gratefully acknowledged.

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Lorenz Klampfl and Franz Wotawa. Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.10

Abstract

Recent advancements in automated and autonomous driving systems have facilitated their integration into modern vehicles, enabling them to accurately perceive their surroundings and support or even fully undertake complex driving tasks. Given the complexity and unpredictable nature of driving environments and traffic situations, ensuring the correct behavior of such systems is essential to prevent hazardous situations, increase user acceptance, and avoid human harm. However, the increased complexity of these systems and the extensive search space of possible scenarios introduce significant challenges to testing and real-time fault management. Hence, besides rigorous testing during the development phase, there is a need for additional validation and verification during operation. This paper proposes utilizing Answer Set Programming (ASP), a form of declarative programming, for continuous real-time monitoring, fault detection, and explanation to ensure the correct functioning of automated and autonomous driving systems. Our approach aims to enhance the reliability and safety of such systems by detecting violations and providing explanations that can support fault-adaptive control or mitigation strategies. We demonstrate the effectiveness of our methodology across diverse scenarios executed within a simulation environment, discuss the main challenges encountered, and outline future research directions.

Subject Classification

ACM Subject Classification
  • General and reference → Validation
Keywords
  • Autonomous Driving
  • Answer Set Programming
  • Continuous Monitoring

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References

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