Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems

Abstract

Modern driver assistance and safety systems are using a combination of two or more sensors for reliable tracking and classification of relevant road users like vehicles, trucks, cars and others. In these systems, processing and fusion stages are optimized for the properties of the sensor combination and the application requirements. A change of either sensor hardware or application involves expensive redesign and evaluation cycles. In this contribution, we present a multi sensor fusion system which is implemented to be independent of both sensor hardware properties and application requirements. This supports changes in sensor combination or application requirements. Furthermore, the environmental model can be used by more than one application at the same time. A probabilistic approach for this generic fusion system is presented and discussed.