Insufficiency-Driven DNN Error Detection in the Context of SOTIF on Traffic Sign Recognition Use Case

Deep Neural Networks (DNNs) are used in various domains and industry fields with great success due to their ability to learn complex tasks from high-dimensional data. However, the data-driven approach within deep learning results in various DNN-specific insufficiencies (e.g., robustness limitations, overconfidence, lack of interpretability), which makes the usage in safety-critical applications, like automated driving, challenging. An important safety strategy to address these limitations is the detection of DNN errors (e.g., false positives) during runtime. In this work, we present a general error detection approach for DNNs, which combines diverse monitoring methods to address different safety-related DNN insufficiencies simultaneously. To ensure consistency with the automotive safety domain, we take into account established concepts of the automotive safety standard ISO 21448 (SOTIF). We apply our error detection method on the safety-related use case of traffic sign recognition by using self-created 3D driving scenarios. In doing so, we consider different types of DNN errors related to in distribution, out of distribution, and adversarial data. We demonstrate that our approach is able to handle all these error types. Furthermore, we show the performance benefit of our method compared to a baseline DNN and to state of the art DNN monitoring methods.