Driver-assistance systems and automated driving applications in the future will require reliable and flexible surround environment perception. Sensor data fusion is typically used to increase reliability and the observable field of view. In this paper, a novel approach to track-to-track fusion in a high-level sensor data fusion architecture for automotive surround environment perception using information matrix fusion (IMF) is presented. It is shown that IMF produces the same good accuracy in state estimation as a low-level centralized Kalman filter, which is widely known to be the most accurate method of fusion. Additionally, as opposed to state-of-the-art track-to-track fusion algorithms, the presented approach guarantees a globally maintained track over time as an object passes in and out of the field of view of several sensors, as required in surround environment perception. As opposed to the often-used cascaded Kalman filter for track-to-track fusion, it is shown that the IMF algorithm has a smaller error and maintains consistency in the state estimation. The proposed approach using IMF is compared with other track-to-track fusion algorithms in simulation and is shown to perform well using real sensor data in a prototype vehicle with a 12-sensor configuration for surround environment perception in highly automated driving applications.