In this master thesis, a transformer-based method is proposed for anomaly detection in multi-agent trajectory data for autonomous driving. An unsupervised reconstruction-based approach is employed to learn a concept of normal driving behavior using vast amounts of normal trajectory data. Based on this approach, anomalous trajectories deviate from the learned notion of normality. The method is evaluated on a benchmark dataset and against a set of state-of-the-art baseline methods.