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Modern cars feature a variety of different driving assistance systems, which aim to improve driving comfort and safety as well as fuel consumption. Due to the technical advances and the possibility to consider vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, cooperative adaptive cruise control (CACC) strategies have received significant attention from both research and industrial communities. The performance of such systems can be enhanced if the future velocity of the surrounding traffic can be predicted. Generally, human driving behavior is a complex process and influenced by several environmental impacts. In this work a stochastic model of the velocity of a preceding vehicle based on the incorporation of available information sources such as V2I, V2V and radar information is presented. The main influences on the velocity prediction considered in this approach are current and previous velocity measurements and traffic light signals.

The plausibility of measurements is a key question for many kinds of large complex systems. In this paper we present a method of automatic measurement data plausibility assessment. Even the focus here was put on an automotive engine test bench application, the presented method can cope also with other complex systems with a large number of measurement devices included. The presented method is still in the development phase, thus its performance still did not reach the optimum. But as for the time being the obtained experimental results seams to be quite acceptable.