A real-time approach to traffic situation representation from image processing data

This paper discusses an approach to provide an environment representation of real traffic scenarios. Image processing is used to get information about the vehicle's position on the road, about detected obstacles in the vicinity and about traffic signs. This incoming information is generated by camera modules which are arranged in a way that a panoramic vision is achieved. The problem is the noisy input data from different image processing modules which makes automated driving and adequate driver warning systems very difficult. The main task to solve this problem is to build up an always consistent situation representation in real time. Situation analysis basically consists of data fusion of different object recognition modules. An object matching algorithm has been developed that takes sensor specific deviations and errors into account. The second part is a rule set that examines the current situation for temporal or spatial inconsistencies using uncertainty representation. The resulting situation description is filtered by a set of Kalman filters which also provide a single step prediction. Apart from the fusion of obstacle recognition data, the situation analysis provides information about the road and currently valid traffic signs.