Today, a huge variety of sensors is built into vehiclesenabling them to obtain a view of their surroundingsbased on locally observed data. With VANETs(vehicular ad-hoc networks) as an emerging technology,it is possible both to disseminate data to othervehicles and to collect information in order to improvetheir own picture of the current situation. However,distributed applications based on VANETs still needto agree on a "common understanding" of context forinteroperability on a contextual level. In recent years,ontologies have become the "silver bullet" for describingconcepts and their relationship in a semanticallyrich manner. In this paper, we apply this approach tocontext modeling and propose a collision avoidanceapplication in order to show that applications canbenefit from the advantages of ontological models likedistributed composition, partial validation, richnessand quality of information and a certain level offormality. Also, we want to prove that calculationson the model are still fast enough to fulfill real-timerequirements imposed by active safety for vehicles.Two different scenarios with different context modelsbut unchanged applications show the advantages ofontologies with respect to extensibility of models. Inaddition, it is shown that larger and more complexmodels do not necessarily increase the execution times.