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Safety Assurance Case Description Method for Systems Incorporating Off‐Operational Machine Learning and Safety Device

Technology in the field of machine learning (ML) has been advanced in recent years. There have been discussions for applying ML technology especially toward safety critical systems such as automated driving. One of the issues is about the safety assurance of the system utilizing ML. Conventional safety assurance methods are not able to assure the system incorporating ML because “it is difficult to estimate its performance in advance, and it is not possible to grasp the boundaries between what can be done and what cannot be done.”(Ishikawa, 2019) Therefore, the purpose of this study is to assure the safety of systems incorporating ML. This study proposes the safety assurance case description method with seven layers for the system incorporating both off‐operational ML and the safety device. Based on the thought of systems engineering, the seven layers utilize the seven viewpoints decomposed with 1) internal and external system, 2) different states of the system, 3) the factors of the accidents in the system, 4) operating conditions of the system, 5) ML and safety device operation process, 6) output rules of the safety devices, and 7) output restriction method of the safety device. The evaluation method for this proposal indicates whether the subjects could describe using both the proposed description method and an example of the train brake system that incorporates off‐operational ML and safety device. The future study topic is the proposal of the description method for the higher layer of the safety device operation process. Moreover, we plan to work on the proposal of the safety assurance case of a system incorporating on‐operational ML.