A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM
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Abstract Faults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.
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Various faults, such as erratic, spike, hard-over, and stuck are examples of sensor faults.
“Various faults, such as erratic, spike, hard-over, and stuck are examples of sensor faults [45].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [45]
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- Authors
- Biddle, L., & Fallah, S.
- Affiliations
- University of Surrey (GB)
- Corresponding author
- Fallah, Saber
- ORCID
- Fallah, Saber
- Venue
- Automotive Innovation
- Published
- 2021-04-05
- Volume
- 4
- Issue
- 3
- Pages
- 301-314
- ISSN
- 2096-4250 (ISSN-L)
- 2522-8765
- OpenAlex
- W3143504837
- Cited by
- 69
- References
- 42
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 14 · 2023: 12 · 2022: 10 · 2021: 2
- FWCI
- 4.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Automotive Innovation (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- fault detection, fault identification, fault prediction, autonomous vehicles, support vector machine, multi-sensor systems, multi-fault diagnosis, real-time implementation, health monitoring, fault propagation, sensor degradation
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