Supporting a SOTIF Safety Argument by Activation Pattern Monitoring with Statistical Guarantees

Rüdiger Ehlers, Loïch Kamdoum Deameni, Nikita Maslov

Abstract: Modern autonomous-driving solutions rely on neural networks for visual perception. They typically lack precise specifications for when their behavior is considered to be correct, which complicates the use of traditional specification-driven verification approaches. To address this challenge, ISO standard 21448 (“Safety of the Intended Functionality”, SOTIF) proposes activities focused on reducing – rather than eliminating – the risk of using machine-learned models and the resulting extent of harm.

External IDs:dblp:conf/vecos/EhlersDM25