Federated cooperative detection of anomalous vehicle trajectories at intersections

Christian Koetsier, Jelena Fiosina, Jan N. Gremmel, Monika Sester, Jörg P. Müller, David M. Woisetschläger

Abstract: Nowadays mobile positioning devices, sensor technologies, machine learning methods and cloud computing allow an efficient collection, processing and analysis of dynamic position information (trajectories), which can be used for various applications. Currently, data owners (e.g. vehicle producers or service operators) are reluctant to share data due to data privacy rules or because of the risk of sharing information with competitors, which could jeopardize the data owner's competitive advantage. In this paper, we compare various state-of-the-art anomaly detection methods like one-class support vector machine, isolation forest and bidirectional generative adversarial networks towards the detection of abnormal vehicle trajectories at intersections solving one-class classification problem with unsupervised learning algorithms. The experiments show that the selected models allow to identify anomalies with 98%-99% accuracy for the centralized approach. However, data exists mostly fragmented in form of isolated islands throughout the whole industry. Thus, we focus on a general concept of collaborative learning which provides with an increasing number of partners more accurate anomaly detection models than local models of each partner without exchanging raw data, but only synchronizing model parameters. We propose a federated learning algorithm for an one-class support vector machine model towards the detection of anomalies. The federated collaborative approach allows to construct comparable accuracy with the centralized approach, reduces the local data labeling load and keeps individual data private.