Increasing traffic density enforces development of Advanced Driver Assistance Systems to cope with safety aspects. Such systems require serious amount of sensor data to deduce spatial relationships. Not only car mounted sensors, but the combination with environmental tracking systems can fulfill the demand for obstacle surveillance. Fusion of sensor data, federation to environmental models and reasoning about that data allows for a broad spectrum of new in-car systems. From collaborative and informing systems up to part or fully automated driving, various assistance systems generate a demand for such spatial knowledge. With an integrated system, dependable data can be delivered to any user-related assistance system, and as side effect, reduce workload in already loaded in-car computer systems. To face these data aggregation and analysis issues, we developed SCORE, a Spatial Context Ontology Reasoning Environment. We illustrate our approach of a distributed ad-hoc infrastructure that collects and disseminates tracking data of environmental objects and thus allows for vehicleand ontology-based reasoning. In extend, we illustrate how such systems can gather data and where such a system can help in spatially related driver assistance systems.     «

Increasing traffic density enforces development of Advanced Driver Assistance Systems to cope with safety aspects. Such systems require serious amount of sensor data to deduce spatial relationships. Not only car mounted sensors, but the combination with environmental tracking systems can fulfill the demand for obstacle surveillance. Fusion of sensor data, federation to environmental models and reasoning about that data allows for a broad spectrum of new in-car systems. From collaborative...     »