Digital Twin (DT) provides a novel idea for Intelligent Transportation Systems (ITS), while Internet of Vehicles (IoV) provides numerous positioning data of vehicles. However, complex interactions between vehicles as well as offset and loss of measurements can lead to tracking errors of DT trajectories. In this paper, we propose a multi-vehicle trajectory tracking framework towards DT intersections (MVT2DTI). Firstly, the positioning data is unified to the same coordinate system and associated with the tracked trajectories via matching. Secondly, a spatial-temporal tracker (STT) utilizes long short-term memory network (LSTM) and graph attention network (GAT) to extract spatial-temporal features for state prediction. Then, the distance matrix is computed as a proposed tracking loss that feeds tracking errors back to the tracker. Through the iteration of association and prediction, the unlabeled coordinates are connected into the DT trajectories. Finally, four datasets are generated to validate the effectiveness and efficiency of the framework.

Multi-Vehicle Trajectory Tracking towards Digital Twin Intersections for Internet of Vehicles

Collotta, M;
2023-01-01

Abstract

Digital Twin (DT) provides a novel idea for Intelligent Transportation Systems (ITS), while Internet of Vehicles (IoV) provides numerous positioning data of vehicles. However, complex interactions between vehicles as well as offset and loss of measurements can lead to tracking errors of DT trajectories. In this paper, we propose a multi-vehicle trajectory tracking framework towards DT intersections (MVT2DTI). Firstly, the positioning data is unified to the same coordinate system and associated with the tracked trajectories via matching. Secondly, a spatial-temporal tracker (STT) utilizes long short-term memory network (LSTM) and graph attention network (GAT) to extract spatial-temporal features for state prediction. Then, the distance matrix is computed as a proposed tracking loss that feeds tracking errors back to the tracker. Through the iteration of association and prediction, the unlabeled coordinates are connected into the DT trajectories. Finally, four datasets are generated to validate the effectiveness and efficiency of the framework.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/158069
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