Introduction -- The Research Domain of this Thesis and its State of the Art -- Theoretical Foundations Relevant to this Thesis -- Situation Feature Relevance on Measurement Data -- Knowledge-Based Traffic Situation Description -- Relevance by Mutual Information on Ontology Features -- Conclusion.

The comprehension of a traffic situation plays a major role in driving a vehicle. Interpretable information forms a basis for future projection, decision making and action performing, such as navigating, maneuvering and driving control. Michael Huelsen provides an ontology-based generic traffic situation description capable of supplying various advanced driver assistance systems with relevant information about the current traffic situation of a vehicle and its environment. These systems are enabled to perform reasonable actions and approach visionary goals such as injury and accident free driving, substantial assistance in arbitrary situations up to even autonomous driving.  Content Situation Feature Relevance on Vehicle Measurement Data Relevance of Historical Measurement Values Knowledge-Based Traffic Situation Description and Simulation Relevance by Mutual Information on Ontology Features Target Groups Researchers, lecturers and students in the fields of automotive engineering, mechatronics, computer science and artificial intelligence Engineers and developers in the automotive industry, specifically areas of driver assistance systems, vehicle control and mechatronics The Author Michael Huelsen completed his doctoral thesis in a cooperation between the Karlsruhe Institute of Technology (KIT) and the Robert Bosch GmbH. After working in automotive development he is now working in a leading position in purchasing and value engineering at a renowned company manufacturing electrical traction systems.