This work provides an approach to create a generic situation description for advanced driver assistance systems using logic reasoning on a traffic situation knowledge base. It contains multiple objects of different type such as vehicles and infrastructure elements like roads, lanes, intersections, traffic signs, traffic lights and relations among them. Logic inference is performed to check and extend the situation description and interpret the situation e. g. by reasoning about traffic rules.The capabilities of our ontological situation description approach are shown at the example of complex intersections with several roads, lanes, vehicles and different combinations of traffic signs and traffic lights. Real-time issues are discussed thereon.

Recently, a demand for advanced driver assistance in sophisticated situations with multiple traffic objects and complex infrastructure has emerged.To handle this kind of situations we developed a knowledge-base to model abstract qualitative information of traffic situations, up to very complex traffic intersections. We further created an asynchronous real-time simulation framework to investigate applicability of knowledge-based driver assistance systems within vehicle or traffic management systems. While the simulation with sensor data is running with very short update cycles the knowledge-base is updated asynchronously since reasoning is time expensive. Driver assistance functions are able to query the knowledge-base once it is fully reasoned.We especially focus on safety assistance systems and performed tests of our knowledge-based framework on exemplary intersection assistance functions. Results show the capability to perform semi and fully autonomous warning and deescalation assistance functions in real-time.