Feasibility study: Rule generation for ontology-based decision-making systems

Abstract

Ontology-based systems can offer enticing benefits for autonomous vehicle applications. One such system is an ontology-based decision-making system. This system takes advantage of highly abstracted semantic knowledge that describes the state of the vehicle as well as the state of its environment. Knowledge on scenario state combined with a set of logical rules is then used to determine correct actions for the vehicle. However, creating a set of rules for this safety-critical application is a challenging problem which must be solved to enable the use of the decision-making system in practical applications. This work explores the feasibility of generating rules for the reasoning system through machine learning. We propose a process for the rule generation and create a set of rules describing vehicle behavior in an uncontrolled four-way intersection.