Abstract
There is a great concern about the quality of current AI, especially DNN (Deep Neural Network), in terms of its safety and reliability. In the case of automated driving, the risk management procedures for systems containing DNNs have been verified. In this paper, we present the whole picture in a demonstrative manner. Also, we show the problems, methods for solving them, and their significance in detail. The system-level safety analysis is connected to the development of DNN model single body, and its development process. Using this method, we aim to build a system that can fix the improvement of DNN repeatedly.
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Acknowledgements
The authors would like to express their sincere thanks to the eAI project for their great cooperation in this research. This research was supported by the Grant-in-Aid for Scientific Research on “Establishment of Accident Analysis Methodology for Socio-technical Systems by System Theory (21K21301)” and the Japan Science and Technology Agency (JST) Project for the Creation of Future Society JPMJMI20B8.
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Kaneko, T., Takahashi, Y., Yamaguchi, S., Hashimoto, J., Yoshioka, N. (2023). Safety and Risk Analysis and Evaluation Methods for DNN Systems in Automated Driving. In: Virvou, M., Saruwatari, T., Jain, L.C. (eds) Knowledge-Based Software Engineering: 2022. JCKBSE 2022. Learning and Analytics in Intelligent Systems, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-031-17583-1_7
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DOI: https://doi.org/10.1007/978-3-031-17583-1_7
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