Generisches Sensorfusionsframework zur gleichzeitigen Zustands- und Existenzschätzung für die Fahrzeugumfelderkennung
Michael Münz · OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) · 2011
The presented work provides a contribution in the field of automotive environment perception based on sensor fusion systems. In contrast to other existing fusion systems, a generic framework is realized, which allows for exchanging any sensor module without the need to adapt the fusion algorithm. This is achieved by modeling sensor specific properties like detection performance or reliability in a probabilistic way, which allows the definition of abstract interfaces between sensor and fusion module. The fusion process is carried out by simultaneously estimating state and existence of objects based on probabilistic information by the sensors. In addition, the resulting environment model can be processed by different driver assistance applications at the same time, which may have unequal needs for reliability of object existence. The requirements for a generic fusion system are analyzed and the need for novel sensor fusion algorithms is described. Existing data fusion algorithms are extended for the claims of the generic fusion system and novel data fusion methods are introduced. Approximations for handling the computationally expensive calculations in real-time are developed which allow ensuring a maximum computing time of the fusion process. Moreover, methods for modeling sensor specific properties, object birth, and object classification are presented. The proposed sensor fusion system is implemented for a specific sensor combination consisting of a laser scanner and a video camera for detecting and tracking vehicles in dense environments like inner city areas. Practical considerations concerning cross-calibration of sensors and timing aspects are provided. The probabilistic sensor models are described in detail. Finally, an exhaustive evaluation of the system based on real-world data is presented and several algorithms are compared. Relevant system parameters like detection performance, tracking time, estimation consistency and state precision are examined.