Detection of anomalous vehicle trajectories using federated learning

Nowadays mobile positioning devices, such as global navigation satellite systems (GNSS) but also external sensor technology like cameras allow an efficient online collection of trajectories, which reflect the behavior of moving objects, such as cars. The data can be used for various applications, e.g., traffic planning or updating maps, which need many trajectories to extract and infer the desired information, especially when machine or deep learning approaches are used. Often, the amount and diversity of necessary data exceeds what can be collected by individuals or even single companies. 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. A promising approach to exploit data from several data owners, but still not directly accessing the data, is the concept of federated learning, that allows collaborative learning without exchanging raw data, but only model parameters.

In this paper, we address the problem of anomaly detection in vehicle trajectories, and investigate the benefits of using federated learning. To this end, we apply several state-of-the-art learning algorithms like one-class support vector machine (OCSVM) and isolation forest, thus solving a one-class classification problem. Based on these learning mechanisms, we successfully proposed and verified a federated architecture for the collaborative identification of anomalous trajectories at several intersections. We demonstrate that the federated approach is beneficial not only to improve the overall anomaly detection accuracy, but also for each individual data owner. The experiments show that federated learning allows to increase the anomaly detection accuracy from in average AUC-ROC scores of 97% by individual intersections up to 99% using cooperation.