A Safety Analysis Method for Perceptual Components in Automated Driving The use of machine learning (ML) is increasing in many sectors of safetycritical software development and in particular, for the perceptual components of automated driving (AD) functionality. Although some traditional safety engineering techniques such as FTA and FMEA are applicable to ML components, the unique characteristics of ML create challenges. In this paper, we propose a novel safety analysis method called Classification Failure Mode Effects Analysis (CFMEA) which is specialized to assess classificationbased perception in AD. Specifically, it defines a systematic way to assess the risk due to classification failure under adversarial attacks or varying degrees of classification uncertainty across the perceptioncontrol linkage. We first present the theoretical and methodological foundations for CFMEA, and then demonstrate it by applying it to an AD case study using semantic segmentation perception trained with the Cityscapes driving dataset. Finally, we discuss how CFMEA results could be used to improve an MLmodel.

Multiunit automotive perception framework: Synergy between AI and deterministic processing Since neural networks were first introduced into automotive systems, safety has been a major concern. The prevailing safety standard in the automotive industry, ISO26262, does not fully define testing and verification methods for software based on deep learning. In this paper, we propose a multiunit perception framework that increases the determinism of automotive systems incorporating deep learning. Our approach relies on ASIL decomposition and algorithm diversification, which are enabled through the utilization of multiple low ASIL perception units and one high ASIL monitor unit. In addition to the framework concept, we specify how each component can be mapped to appropriate hardware and software platforms. The practical feasibility of the perception framework is demonstrated with a proof of concept prototype.