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While Deep Neural Networks (DNN) have revolutionized applications that rely on computer vision, their characteristics introduce substantial challenges to automotive safety engineering. The behavior of a DNN is not explicitly expressed by an engineer in source code, instead enormous amounts of annotated data are used to learn a mapping between input and output. Functional safety as defined by ISO 26262 is not sufficient to match the needs for the new generation of data-driven software.
Earlier this year, ISO/PAS 21148 Safety of the Intended Functionality (SOTIF) was published by ISO. SOTIF is a Publicly Available Specification (PAS), a response to a pressing need of an automotive safety standard appropriate for machine learning. A PAS is a stepping stone toward a new ISO standard, and SOTIF is intended to complement conventional functional safety as defined in ISO 26262.
In this presentation, we introduce the SOTIF process and present our contributions on how to support safety of the intended function. First, we present search-based software testing to efficiently and effectively idenify test scenarios that cause safety violations in simulated environments. Second, we present a safety cage architecture that helps percepiont systems reject input that does not resemble the training data.