Framework for safety assessment of autonomous driving functions up to SAE level 5 by self-learning iteratively improving control loops between development, safety and field life cycle phases
IEEE ICCP 2021, 17th International Conference on Intelligent Computer Communication and Processing
28 - 30 October 2021
Häring, I.; Lüttner, F.; Frorath, A.; Fehling-Kaschek, M.; Ross, K.; Schamm, T.; Knoop, S.; Schmidt, D.; Schmidt, A.; Ji, Y.; Yang, Z.; Rupalla, A.; Hantschel, F.; Frey, M. ; Wiechowski, N.; Schyr, C.; Grimm, D.; Zofka, M. R.; Viehl, A.
Safety verification and validation of autonomous driving functions up to SAE level 5 pose enormous challenges for car manufacturers. The paper argues that efficient improvement opportunities arise by suitably combining iterative development and verification processes that use self-learning approaches and well-defined quality and convergence criteria within a conceptual framework. The following cycles are used: development cycle, safety life cycle and field life cycle. For these cycles, suitable phases are first identified and defined. Then linkages are given that enable criteria-based iterative execution and improvement of selected combined phases. For this purpose, the selected phases are further resolved. It is distinguished between local loops within one cycle and loops between several cycles as well as with respect to the time horizon they cover. Suitable sample machine learning (ML) and artificial intelligence (AI) methods for the improvement loops are proposed in order to improve safety assessment of autonomous driving (AD) functions. The article presents three different types of ML/AI approaches regarding their usage within the development process of AD functions as well as identifies further improvement potentials. The approach is illustrated by ML/AI approach examples for the efficient provision of relevant and critical scenarios for the training and assessment of AD functions. Keywords-Verification and validation of autonomous driving, iterative self-learning improvement loop, machine learning and artificial intelligence, selection of training data, development, safety and life cycle.