TY - GEN

T1 - Federated cooperative detection of anomalous vehicle trajectories at intersections

AU - Koetsier, Christian

AU - Fiosina, Jelena

AU - Gremmel, Jan N.

AU - Sester, Monika

AU - Müller, Jörg P.

AU - Woisetschläger, David

N1 - Funding Information: The research was funded by the Lower Saxony Ministry of Science and Culture under grant number ZN3493 within the Lower Saxony “Vorab“ of the Volkswagen Foundation and supported by the Center for Digital Innovations.

PY - 2021/11/18

Y1 - 2021/11/18

N2 - 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.

AB - 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.

KW - anomaly detection

KW - federated learning

KW - machine learning

KW - vehicle trajectories

UR - http://www.scopus.com/inward/record.url?scp=85121096555&partnerID=8YFLogxK

U2 - 10.1145/3486626.3493439

DO - 10.1145/3486626.3493439

M3 - Conference contribution

AN - SCOPUS:85121096555

SP - 13

EP - 22

BT - Proceedings of the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2021

A2 - Kar, Bandana

A2 - Mohebbi, Shima

A2 - Fu, Guangtao

A2 - Ye, Xinyue

A2 - Omitaomu, Olufemi A.

T2 - 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2021

Y2 - 2 November 2021

ER -