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With the recent developments of intelligent transportation systems and intelligent vehicles, data fusion gains more and more attention as a technique to make optimal use of the various in‐vehicle and roadside sensors. Applications include various approaches for object and incident detection, state estimation, and state forecasting. In general, data fusion can be described as the intelligent processing and combination of information from many diverse sources. In particular, data fusion is applied to gather information by combining various sources that is of higher value than information from only one source. This chapter gives an overview of data fusion methods and applications from the vehicular as well as the roadside perspectives. First of all, an introduction of the main concepts of data fusion is given. Then, commonly used methods are described. The main part consists of two sample applications, one from the area of vehicle automation and the other from the area of traffic state estimation and prediction. Finally, a summary and an outlook on sensor data fusion are given.

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