As the greenhouse effect is an imminent concern, motivation for the development of energy efficient systems has grown fast. Today heavy-duty vehicles (HDVs) account for a growing part of the emissions from the vehicular transport sector. One way to reduce those emissions is by driving at short intervehicular distances in so called platoons, mainly on highways. In such formations, the aerodynamic drag is decreased which allows for more fuel efficient driving, meanwhile the roads are used more efficiently. This thesis deals with the question of how those platoons can be controlled without using communications between the involved HDVs. In this thesis, artificial neural networks are designed and trained to predict the velocity profile for an HDV driving over a section of road where data on the topography are available. This information is used in a model predictive controller to control the HDV driving behind the truck for which the aforementioned prediction is made. By having accurate information about the upcoming behaviour of the preceding HDV, the controller can plan the velocity profile for the controlled HDV in a way which minimizes fuel consumption. To ensure fuel optimal performance, a state describing the mass of consumed fuel is derived and minimized in the controller. A system modelling gear shift dynamics is proposed to capture essential dynamics such as torque loss during shifting. The designed controller is able to predict and change between the three highest gears making it able to handle almost all highway platooning scenarios. The prediction system shows great potential and is able to predict the velocity profile for different HDVs with an average error as low as 0.04 km/h. The controller is implemented in a simulation environment and results show that compared to a platoon without these predictions of the preceding HDV, the fuel consumption for the controlled HDV can be reduced by up to 6 %.