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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.

Although SCR is a well established technology for many applications, it is still a field in which several new approaches and components are being tested. Control is a critical issue, as the conflicting requirements of NOx abatement and very small NH3 slip need to be met. Besides empirical solutions, model based controls have been proposed and are probably the technology of choice, also in view of the combination with monitoring functions. However, SCR models are typically based on First Principles (FP), i.e. on global chemical equations and reaction rate equations, and require precise calibration. Still, their performance for the control of dynamic processes is limited, or a high detail, much a priori information, e.g. on the actual SCR reaction rates, are needed. Frequently, this information is not available or reliable, and this is particularly true when components are changed or modified during the development process, so that typically a re-design is needed.