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Software diagnostics for autonomous safety-critical control-systems based on artificial intelligence

Javier Fernández Muñoz


  • DIRECTORS: Irune Agirre Troncoso and Jaume Abella Ferrer 
  • UNIVERSITY: Universitat Politècnica de Catalunya (UPC)


Machine Learning (ML) systems allow the efficient implementation of functionalities that can be hard to program by traditional software due to the high spectrum of inputs that hinder the definition of a specific procedural rule set. This characteristic of ML systems has encouraged their adoption in applications such as object detection or image classification in several safety-related domains, which are subject to safety certification. This certification is usually achieved by adhering to traditional functional safety standards such as IEC 61508 or ISO 26262. However, these standards were not devised to accommodate technologies such as ML in safety-related systems due to their development process, which is based on probabilistic models generated from training data, as opposed to traditional software components coded from specifications. Additionally, new challenges arise due to the fact that these ML algorithms need to process large volumes of data, and this requires High-Performance Embedded Computing (HPEC) platforms with computing capabilities far superior to traditional safety systems, such as multicore devices and GPU accelerators. Current functional safety standards do not provide explicit guidance for the use of HPEC platforms in safety-relevant systems, and the inherent complexity of those highly parallel architectures challenges certifications. With this Thesis, we attempt to address these challenges and give a step forward towards the functional safety certification of safety control systems integrating ML components in HPEC platforms.

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