Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest

Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest

According to ISO 26262 standard, functional validation of the developed Automotive Software Systems (ASSs) is crucial to ensure the safety and reliability aspects. Hardware-in-the-loop (HIL) has been introduced as a reliable, safe and flexible test platform to enable the validation process in real-t...

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Bibliographic Details
Main Authors: Mohammad Abboush, Christoph Knieke, Andreas Rausch
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Automotive software systems
fault detection and diagnosis
deep learning
denoising autoencoder
LSTM
random forest
Online Access:https://ieeexplore.ieee.org/document/10348584/

Internet

https://ieeexplore.ieee.org/document/10348584/

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