Orf, Stefan (FZI Research Center for Information Technology), Ochs, Sven (FZI Research Center for Information Technology), Marotta, Valentin (FZI Research Center for Information Technology), Conder, Oliver (FZI Research Center for Information Technology), Zofka, Marc René (FZI Research Center for Information Technology), Zöllner, J. Marius (FZI Research Center for Information Technology; KIT Karlsruhe In)

Functionality Assessment Framework for Autonomous Driving Systems Using Subjective Networks

Scheduled for presentation during the Regular Session "S42a-Safety and Risk Assessment for Autonomous Driving Systems" (FR-LM-T42), Friday, November 21, 2025, 11:10−11:30, Broadbeach 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of individual components an isolated glimpse into the system is given. Literature provides several of these detached assessments, often in the form of safety or performance measures. But dependencies, redundancies, error propagation and conflicting functionality statements do not allow for easy combination of these measures into a big picture of the functioning of the entire AD stack. Data is processed and exchanged between different components, each of which can fail, making an overall statement challenging. The lack of functionality assessment frameworks that tackle these problems underlines this complexity.

This article presents a novel framework for inferring an overall functionality statement for complex component based systems by considering their dependencies, redundancies, error propagation paths and the assessments of individual components. Our framework first incorporates a comprehensive conversion to an assessment representation of the system. The representation is based on Subjective Networks (SNs) that allow for easy identification of faulty system parts. Second, the framework offers a flexible method for computing the system's functionality while dealing with contradicting assessments about the same component, dependencies, and redundancies. We discuss the framework's capabilities on real-life data of our AD stack with assessments of various components.