Supervising the safe operation of automated vehicles is a key requirement in order to unleash their full potential in future transportation systems. In particular, previous publications have argued that SAE Level 4 vehicles should be aware of their capabilities at runtime to make appropriate behavioral decisions. In this paper, we present a framework that enables the implementation of an online capability monitor. We derive a graphical system model that captures the relationships between the quality of system elements across different architectural views. In an expert-driven approach, we parameterize Bayesian Networks based on this structure using Fuzzy Logic. Using the online monitor, we infer the quality of the system's capabilities based on technical measurements acquired at runtime.
@article{arxiv.2404.16502,
title = {A Prototypical Expert-Driven Approach Towards Capability-Based Monitoring of Automated Driving Systems},
author = {Richard Schubert and Cedrik Kaufmann and Marcus Nolte and Markus Maurer},
journal= {arXiv preprint arXiv:2404.16502},
year = {2024}
}
Comments
Accepted for 27th IEEE International Conference on Intelligent Transportation Systems