Developing autonomous vehicles is a challenging task. One of the reasons for this is that human behavior is unpredictable. Another reason is that this problem is in a high-risk environment. Most traffic accidents are due to human error. Thus a conclusion can be made that autonomous vehicles will make driving safer. One type of accident happens when one participant changes the lane and the other participant doesn't notice it. If a system could predict lane change and alert the driver in time it could prevent an accident. Deep learning methods are state of the art approach to prediction problems. Neural networks are, however, known as black-box models. This is the reason they are not fully suitable for high-risk domains such as traffic environment. This thesis will take an alternative approach to lane-change maneuver prediction. This approach is called Bayesian or probabilistic machine learning. There are two main benefits to this approach. First is interpretability of probabilistic models and second is good uncertainty representation. We will create a Bayesian network for predicting lane change maneuvers of traffic participants. We will look at Highway Drone Dataset (HighD) and show conclusions. From this dataset, we will create a training and test dataset. To create a model we will use a probabilistic programming language called pyro. We will train the model on a training set using an algorithm called Black Box Variational Inference. After the training, the model is evaluated and evaluation metrics are reported. The inference time is appropriate for real-time implementation. Prediction power is comparable with other probabilistic approaches but worse than deep learning models.