2021/08/30 by Rick Salay, Salay, Rick, Krzysztof Czarnecki +16 · 1 citation
Computer Science · Decision Sciences · Engineering · Psychology · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Electrical engineering #Human-Automation Interaction and Safety #Machine Learning (cs.LG) #Risk and Safety Analysis #Robotics (cs.RO) #Safety Systems Engineering in Autonomy #Software Engineering (cs.SE) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2108.13294
openalex publication_date 2021/08/30 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Safety assurance is a central concern for the development and societal\nacceptance of automated driving (AD) systems. Perception is a key aspect of AD\nthat relies heavily on Machine Learning (ML). Despite the known challenges with\nthe safety assurance of ML-based components, proposals have recently emerged\nfor unit-level safety cases addressing these components. Unfortunately, AD\nsafety cases express safety requirements at the system level and these efforts\nare missing the critical linking argument needed to integrate safety\nrequirements at the system level with component performance requirements at the\nunit level. In this paper, we propose the Integration Safety Case for\nPerception (ISCaP), a generic template for such a linking safety argument\nspecifically tailored for perception components. The template takes a deductive\nand formal approach to define strong traceability between levels. We\ndemonstrate the applicability of ISCaP with a detailed case study and discuss\nits use as a tool to support incremental development of perception components.\n