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The deployment of predictive driving styles reduces fuel consumption of vehicles significantly, while assistance systems can support drivers in this task. This paper describes a modular approach to consider various sources of information as well as different driver and vehicle types in the prediction and the optimization of the vehicle's longitudinal dynamics to reduce fuel consumption. Energy efficient driving strategies such as roll out or fuel cut-off are compared to the average driving behavior of the driver. The utility of the efficient strategies is assessed relative to the average driver behavior, which is similar to human information processing. Resulting optimal driving strategies are provided to the driver as recommendations or applied to vehicles by intervening assistance systems such as adaptive cruise control. This paper aims to summarize the basic methodology of the approach.