Detection of anomalous vehicle trajectories using federated learning
Open accessAbstract
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.
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The approach in [62] supported continuous traffic adaptation, improving detection accuracy while preserving privacy.
“This approach supported continuous traffic adaptation, improving detection accuracy while preserving privacy.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [62]
The Conclusion reports improved anomaly-detection accuracy through cooperation and says individual data remain private because participants exchange model parameters. It does not establish that the approach continuously adapts to changing traffic conditions; the dynamic adaptation mentioned in Related work concerns a different approach. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn [62], FL was integrated with the one-class support vector machine (OC-SVM) for anomalous driving behavior detection at intersection points.
“In [62], FL was integrated with the one-class support vector machine (OC-SVM) for anomalous driving behavior detection at intersection points.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [62]
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- Authors
- Koetsier, C., Fiosina, J., Gremmel, J. N., Müller, J. P., Woisetschläger, D. M., & Sester, M.
- Affiliations
- Leibniz University Hannover (DE)
- Technische Universität Braunschweig (DE)
- Clausthal University of Technology (DE)
- Corresponding author
- Koetsier, Christian
- Venue
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 4
- Pages
- 100013
- ISSN
- 2667-3932 (ISSN-L)
- OpenAlex
- W4220979562
- Cited by
- 31
- References
- 57
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 8 · 2023: 9 · 2022: 1
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ISPRS Open Journal of Photogrammetry and Remote Sensing (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Niedersächsische Ministerium für Wissenschaft und Kultur (ZN3493)
- Volkswagen Foundation (ZN3493)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Keywords
- federated learning, one-class classification, one-class support vector machine, Isolation Forest, data privacy, cooperative learning, traffic intersections
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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