Today, a huge variety of sensors is built into vehicles enabling them to obtain a view of their surroundings based on locally observed data. With VANETs (vehicular ad-hoc networks) as an emerging technology, it is possible both to disseminate data to other vehicles and to collect information in order to improve their own picture of the current situation. However, distributed applications based on VANETs still need to agree on a "common understanding" of context for interoperability on a contextual level. In recent years, ontologies have become the "silver bullet" for describing concepts and their relationship in a semantically rich manner. In this paper, we apply this approach to context modeling and propose a collision avoidance application in order to show that applications can benefit from the advantages of ontological models like distributed composition, partial validation, richness and quality of information and a certain level of formality. Also, we want to prove that calculations on the model are still fast enough to fulfill real-time requirements imposed by active safety for vehicles. Two different scenarios with different context models but unchanged applications show the advantages of ontologies with respect to extensibility of models. In addition, it is shown that larger and more complex models do not necessarily increase the execution times.