A Knowledge-based Approach for the Automatic Construction of Skill Graphs for Online Monitoring
This addresses the need for reliable capability monitoring in automated vehicle development, though it is an incremental improvement over existing skill graph methods.
The paper tackles the problem of manual, error-prone skill graph construction for automated vehicles by proposing a knowledge-based approach to automate the process, resulting in reduced inconsistencies and errors when reflecting changes in the operational design domain.
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.