Abstract Situation understanding is a key factor for future advanced driver assistance systems (ADAS) to handle complex traffic situations. An important part is the prediction of future actions of other traffic participants. Tackling this problem the paper at hand focuses on the prediction of lane change maneuvers on highways. A novel approach is presented that first separates the prediction into a situation based and a movement based approach and fuses them afterwards. A situation based probability resting on inter-vehicle relations is derived, which enables very early reasoning about the current traffic situation and gives prior knowledge about possible lane change maneuvers. Then observations of the vehicles lateral movement inside the lane are used to perform a probabilistic multi-class classification with a Support Vector Machine (SVM). Both probabilities are combined to enhance the movement based result using the situation related probability as prior knowledge from the current driving situation. The approach is tested on a dataset recorded on a fixed-base driving simulator. Considering only the situation based information early prediction of a feasible lane change is possible. Furthermore the evaluation affirms the improvement of the prediction in case the approach is extended to incorporate both probabilities. Finally the combined approach is tested on a dataset recorded in highway traffic scenarios.