Predicting vehicle maneuvers is a critical task for developing autonomous driving. These maneuvers have been identified as leading causes of fatal accidents, underscoring the need for robust and reliable detection systems. This thesis addresses this critical issue by developing and evaluating novel methodologies for classifying maneuvers, especially lane change and cut-in maneuvers in front of the vehicle. Two specific methods are proposed in this thesis work, and their effectiveness is evaluated on two datasets: the Prevention Lane Change Prediction dataset and the BDD-100K Cut-in/Lane-pass Classification Subset. The first method is a model that utilizes features extracted from the bounding boxes of the target vehicle, feeding them into a single-layer LSTM network for cut-in/lane-pass classification. The second method involves training a 3-dimensional residual neural network in a self-supervised manner using contrastive video representation learning. For the self-supervised training phase, a novel scene representation is proposed to highlight vehicle motions. Afterward, the same model is fine-tuned using labeled video data. Lastly, an ensemble learning approach is introduced, which combines the predictive capabilities of the proposed LSTM-based and self-supervised contrastive video representation learning models, leveraging the strengths of both methods to enhance the overall maneuver classification performance. The proposed methods made significant contributions to the field. The LSTMbased model achieved high classification accuracies compared to other studies in the literature. The self-supervised video representation learning model represents the first application of contrastive learning in maneuver classification. The ensemble learning approach has shown a significant improvement in the performance of the maneuver detection system.