Ontology-based traffic scene modeling, traffic regulations dependent situational awareness and decision-making for automated vehicles
Martin Buechel, Gereon Hinz, Frederik Ruehl, Hans Schroth, Csaba Gyoeri, Alois Knoll · 2017
This paper presents a modular framework for traffic regulations based decision-making of automated vehicles. It builds on a semantic traffic scene representation formulated as ontology and includes knowledge about traffic regulations. The semantic representation supports traffic situation classification by reasoning, providing improved situational awareness for the automated vehicle. Decision-making rules are directly derived from traffic regulations and concepts used in the ontology are harmonized with concepts used in traffic regulations. Due to the modular structure of the developed ontology, switching between different sets of national traffic regulations becomes a simple process. The methodology is evaluated for a variety of traffic scenarios, building up from basic to complex urban scenarios containing intersections, traffic regulating police officers and crossing street railways.