# A Knowledge-based Approach for the Automatic Construction of Skill Graphs for Online Monitoring

arXiv: https://arxiv.org/abs/2102.08827 · HTML: https://mlsanity.com/paper/2102.08827/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Ontology based Scene Creation for the Development of Automated Vehicles](https://mlsanity.com/paper/1704.01006/) (2017)
- [Towards a Skill- And Ability-Based Development Process for Self-Aware Automated Road Vehicles](https://mlsanity.com/paper/1708.02532/) (2017)
- [A System's Perspective Towards an Architecture Framework for Safe Automated Vehicles](https://mlsanity.com/paper/1804.07020/) (2018)
- [Supporting Safe Decision Making Through Holistic System-Level Representations & Monitoring -- A Summary and Taxonomy of Self-Representation Concepts for Automated Vehicles](https://mlsanity.com/paper/2007.13807/) (2020)

## Built on by (most cited first, 2 of 2)

- [Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving](https://mlsanity.com/paper/2205.01532/) (2022)
- [Towards Scenario- and Capability-Driven Dataset Development and Evaluation: An Approach in the Context of Mapless Automated Driving](https://mlsanity.com/paper/2404.19656/) (2024)

## Abstract (arXiv)

Automated vehicles need to be aware of the capabilities they currently possess. Skill graphs are directed acylic graphs in which a vehicle's capabilities and the dependencies between these capabilities are modeled. The skills a vehicle requires depend on the behaviors the vehicle has to perform and the operational design domain (ODD) of the vehicle. Skill graphs were originally proposed for online monitoring of the current capabilities of an automated vehicle. They have also been shown to be useful during other parts of the development process, e.g. system design, system verification. Skill graph construction is an iterative, expert-based, manual process with little to no guidelines. This process is, thus, prone to errors and inconsistencies especially regarding the propagation of changes in the vehicle's intended ODD into the skill graphs. In order to circumnavigate this problem, we propose to formalize expert knowledge regarding skill graph construction into a knowledge base and automate the construction process. Thus, all changes in the vehicle's ODD are reflected in the skill graphs automatically leading to a reduction in inconsistencies and errors in the constructed skill graphs.