Scene Understanding of Urban Road Intersections with Description Logic

Road recognition from video sequences has been solved robustly only for small, often simplified subsets of possible road configurations. This contribution argues for a massive augmentation of the amount of prior knowledge to enable the development of more generally applicable estimators. Description Logic is introduced as an expressive knowledge representation formalism for scene understanding. A Description Logic knowledge base for a wide class of road and intersection configurations is set up. Sensor data from an in-vehicle vision sensor and from a digital map provide evidence for a particular intersection. Partial observability and different abstraction layers of the input data are naturally handled by the formalism. Deductive inference services are used to narrow down the intersection hypothesis space based on the evidence and the background knowledge, and to retrieve intersection information relevant to a user, i.e. a human or a driver assistance system.