The missing link: Developing a safety case for perception components in automated driving

• URL: http://arxiv.org/abs/2108.13294v1
• Date: Mon, 30 Aug 2021 15:12:27 GMT
• Title: The missing link: Developing a safety case for perception components in automated driving
• Authors: Rick Salay, Krzysztof Czarnecki, Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae, Vahdat Abdelzad, Chengjie Huang, Maximilian Kahn, Van Duong Nguyen
• Abstract summary: Perception is a key aspect of automated driving systems (AD) that relies heavily on Machine Learning (ML) Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. We propose a generic template for such a linking argument specifically tailored for perception components.
• Score: 10.43163823170716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system-level and these efforts are missing the critical linking argument connecting safety requirements at the system-level to component performance requirements at the unit-level. In this paper, we propose a generic template for such a linking argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels. We demonstrate the applicability of the template with a detailed case study and discuss its use as a tool to support incremental development of perception components.

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