A knowledge-based approach to behavior decision in intelligent vehicles

Recent advances in the field of intelligent vehicles have shown that it is possible nowadays to provide the driver with useful assistance systems, or even letting a car drive autonomously over long distances on highways. Usually these approaches are on a rather quantitative level. A knowledge-based approach as presented here has the advantage of a better comprehensibility and allows for formulating and using common sense knowledge and traffic rules while reasoning. In our approach a knowledge base is the central component for higher-level functionality. A qualitative mapping module abstracts from the quantitative data and stores symbolic facts in the knowledge base. The knowledge-based approach allows for easily integrating and adjusting background knowledge. Higher-level modules can query the knowledge base in order to evaluate the situation and decide what actions to perform. For the evaluation of the approach a prototype was developed in order to simulate traffic scenarios. In experiments behavior decision was applied for controlling the vehicle and its gaze.