Facilitating Change Implementation for Continuous ML-Safety Assurance

Chih-Hong Cheng; Nguyen Anh Vu Doan; Balahari Balu; Franziska Schwaiger; Emmanouil Seferis; Simon Burton; Yassine Qamsane; Ankit Shukla; Yinchong Yang; Zhiliang Wu; Andreas Hapfelmeier; Ingo Thon

arXiv · 2209.11632

Facilitating Change Implementation for Continuous ML-Safety Assurance

Chih-Hong Cheng·Nguyen Anh Vu Doan·Balahari Balu·Franziska Schwaiger·Emmanouil Seferis·Simon Burton·Yassine Qamsane·Ankit Shukla·Yinchong Yang·Zhiliang Wu·Andreas Hapfelmeier·Ingo Thon

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Abstract

We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering process is desired. We associate semantic tags with the safety case argumentation and turn each piece of evidence into a quantitative metric or a logic formula. With proper tool support, the impact can be characterized by a query over the safety argumentation tree to highlight evidence turning invalid. The concept is exemplified using a vision-based emergency braking system of an autonomous guided vehicle for factory automation.

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Chih-Hong Cheng, Nguyen Anh Vu Doan, Balahari Balu, Franziska Schwaiger, Emmanouil Seferis, Simon Burton, Yassine Qamsane, Ankit Shukla, Yinchong Yang, Zhiliang Wu, Andreas Hapfelmeier, Ingo Thon. 2022-09-23. Facilitating Change Implementation for Continuous ML-Safety Assurance. https://arxiv.org/abs/2209.11632

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