A Feature-Based Machine Learning Approach for Mixed-Criticality Systems

Driven by the recent technological advancements in the field of artificial intelligence, machine learning has emerged as a promising representation learning and decision-making method in many technological domains. Inspired by impressive these results, now machine learning techniques are also being applied to address the decision-making and control problems in the area of cyber-physical systems. For instance, some of these systems fall under the category of safety-critical systems such as chemical plants, autonomous vehicles, surgical robots, and modern medical equipment. One of the major performance issues related to the applicability of machine learning with safety-critical systems is related to the probability-based prediction nature of machine learning components used within such systems. This particular characteristic of machine learning makes it extremely difficult to guarantee safety as directed by standards such as ISO 26262. More importantly, the non-transparent and complex nature of machine learning algorithms make both the reasoning as well as formally establishing the safety aspects of the underlying system extremely difficult. The objective of this research work is to investigate on this key issue, and further on propose an efficient machine learning methodology based on the mixed-criticality approach feasible to safety-critical systems.