Trajectory prediction and intent recognition in confluent waters based on RBF neural network

Xiaoxuan Li; Keping Guan

doi:10.1117/12.3024780

14 February 2024 Trajectory prediction and intent recognition in confluent waters based on RBF neural network

Xiaoxuan Li, Keping Guan

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 1301837 (2024) https://doi.org/10.1117/12.3024780
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

An investigation was conducted to predict ship trajectories and discern their navigational intent in intersecting waters. The study involved experiments utilizing AIS data near the Baoshan District, Shanghai, and the development of the RBFNN+KNN model. The data preprocessing steps encompassed: 1) cleaning abnormal trajectory points and routes; 2) employing the LOF algorithm to filter trajectories with significant outliers; 3) clustering ship trajectories. Two clusters, representing routes in the straight-ahead channel and the right-turn channel, were selected as the sample dataset. The RBF neural network was employed to forecast ships' trajectories in future time periods, and the KNN classification algorithm was integrated to determine the vessels' sailing intentions. The experimental outcomes demonstrated that the proposed method achieved 99.88% accuracy in navigational intent recognition for the training set and 99.18% for the test set. These results indicate the model's superior ability to predict ships' navigational intent.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoxuan Li and Keping Guan "Trajectory prediction and intent recognition in confluent waters based on RBF neural network", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 1301837 (14 February 2024); https://doi.org/10.1117/12.3024780

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