Jan Sparbert

IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision

This paper focuses on recognition and tracking of maneuvering vehicles in dense traffic situations. We present an asynchronous multi obstacle multi sensor tracking method that fuses information from radar and monocular vision. A low level fusion method is integrated into the framework of an IMMPDA Kalman filter. Real world experiments demonstrate that the system combines the complementary strengths of the employed sensors.

Sensor Data Fusion for Active Safety Systems

Active safety systems will have a great impact in the next generation of vehicles. This is partly originated by the increasing consumers interest for safety and partly by new traffic safety laws. Control actions in the vehicle are based on an extensive environment model which contains information about relevant objects in vehicle surroundings. Sensor data fusion integrates measurements from different surround sensors into this environment model. In order to avoid system malfunctions, high reliability in the interpretation of the situation, and therefore in the environment model, is essential. Hence, the main idea of data fusion is to make use of the advantages of using multiple sensors and different technologies in order to fulfill these requirements, which are especially high due to autonomous interventions in vehicle dynamics (e. g. automatic emergency braking). The technical challenge in the development of a serial product relies in the implementation with given sensors, as well as in the risk assessment of the system.