Multi-sensor systems in automotive safety applications and sensor data fusion have become very popular in recent years. Sensors on board cars and active safety applications are increasing in number and the need to define a common method for object extraction and serving these applications has been recognized. Authors propose a high level fusion approach suitable for automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper this track level data fusion approach is introduced with the main focus to be on the data association of tracks coming from the on board sensors with distributed processing. The core of the proposed approach is the formulation of the data association problem in presence of multipoint objects and then the solution for (a) multidimensional assignment and (b) all around vehicle object maintenance. The motivation of this work is the research work that is carried out in the project PReVENT/ProFusion2 where the proposed algorithm is being tested in two experimental vehicles.