<p dir="ltr">Distracted driving is at the forefront of the leading causes of road accidents. Therefore, research advancements in Driver Monitoring Systems (DMS) are vital in facilitating prevention techniques. These systems must be able to detect anomalous driving behavior by evaluating deviations from some predefined normal driving behavior. This thesis proposes an improved contrastive learning approach that introduces a hybrid loss function combining triplet loss and supervised contrastive loss, as well as improvements to the projection head of the framework. It progresses the architecture by performing a multi-threshold severity calculation and data processing using an exponential moving average technique. Due to the unbounded possibilities of anomalous driving behaviors, the proposed framework was tested on the Driver Anomaly Detection (DAD) dataset that incorporates multi-modal and multi-view inputs in an open set recognition setting. The test set of the DAD dataset has anomalous actions that are unseen by the trained model; therefore, high precision on such a dataset demonstrates success on any other closed-set recognition task. The proposed framework achieved an impressive accuracy, reaching 94.14\%, AUC-ROC at 0.9787, and AUC-PR at 0.9781 on the test set. These findings contribute to in-vehicle monitoring by providing a scalable and adaptable framework suitable for real-world conditions.</p>