The concern for more energy efficient vehicles has grown in the last years; the increase of oil prices and the need to curb greenhouse gas emissions has called for the search of solutions and alternatives to the fossil-fuel usage. 

On one hand there is the search for better fuel sources while on the other, the development for better driver assistance systems These systems should be capable of maximizing the driver's security and comfort while minimizing the fuel consumption. This can be achieved by the understanding of the environment surrounding the vehicle. Therefore the assistance should use this knowledge of its environment to alert the driver from any situation of risk so that they can be prevented. Moreover, reducing the number of accidents and unnecessary maneuvers will help reduce the fuel consumption by avoiding emergency breaking and other unnecesary situations. 

This thesis explains the development of a maneuver prediction system for highway scenarios. The system should identify lane changing vehicles with enough time to allow the driver to take the necessary precautions and maintain an efficient and more secure driving. To achieve this gial a set of features that describe the environment around the Ego vehicle is obtained. Afterwards, these Features are analyzed using state of the art data mining techniques and their performance is evaluated using a set of classification algorithms (Linear Discriminant Analysis, Neural networks and Random Forests). 

According to the present set of input data, it is possible to identify a left lane change from a right lane change with low misclassification error. But it is not possible to identify with the same efficiency a lane change from a no lane change. The best performance was obtained with a random forest where half of the lane changes are recognized while at the same time achieving a low number of false alarms; in another test, using a sub set of data by filtering noisy observations, it was possible to recognize left and right lane changes with more than 70\% efficiency.