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Highly automated driving brings the next generation of driver assistance systems for increased safety and comfort. Automated vehicles execute part of the driving tasks whereas the driver is still involved in controlling the vehicle. Higher degrees of automation pose more strict requirements for perception systems in terms of performance and robustness. The HAVEit EU project investigates the application and validation of highly automated vehicle systems, technologies that are going to have a great impact on transport of the future. The purpose of this study is to examine in detail the problem of multi-sensor fusion for target tracking and road environment perception in an automated vehicle application. A series of algorithms are described for solving the data association and track estimation problems, both at sensor and central levels. The techniques that are used for multi-sensor lane estimation are also presented. Finally, results from simulated and real-time tests are given to demonstrate the performance of the algorithm.