Trajectory Prediction of Port Container Trucks Based on DeepPBM-Attention

Haixiong YE; Kairong LUAN; Mei YANG; Xiliang ZHANG; Yue ZHOU

doi:10.7307/ptt.v36i3.420

Authors

Haixiong YE College of Engineering Science and Technology, Shanghai Ocean University
Kairong LUAN College of Engineering Science and Technology, Shanghai Ocean University
Mei YANG Shanghai International Port (Group) Co., Ltd.; Academy for Engineering and Technology, Fudan University
Xiliang ZHANG School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University
Yue ZHOU College of Engineering Science and Technology, Shanghai Ocean University

DOI:

https://doi.org/10.7307/ptt.v36i3.420

Keywords:

port container trucks, trajectory prediction, population-based training, deep long short-term memory

Abstract

Existing tracking algorithms mostly rely on model-driven approaches, which can be prone to inaccuracies due to unpredictable human behaviours. This article aims to address the issue of transient errors in tracking port container trucks (PCTrucks) when encountering obstructions. A data-driven algorithm for predicting vehicle trajectories is proposed in this study. The approach involves preprocessing an extensive dataset of GPS information, training a DeepLSTM-Attention model, and integrating the proposed model with the population-based training (PBT) algorithm to optimise network hyperparameters. The objective is to enhance the accuracy of predicting trajectories for vehicles moving horizontally. The trajectory data used are collected from real-world port operations. This research is conducted across nine trajectory segments and benchmarked against traditional approaches like Kalman filtering, machine learning techniques such as support vector regression (SVR) and standard long short-term memory (LSTM) networks. The results demonstrate that the proposed prediction method, that is, DeepPBM-Attention, outperforms other techniques in several evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), F1 score and trajectory reconstruction error (TRE). Compared to LSTM networks, the performance of DeepPBM-Attention is improved by approximately 40%. The proposed data-driven trajectory prediction algorithm exhibits high accuracy and practicality, which can effectively be applied to the positioning prediction of horizontally moving vehicles in port environments.

References

Park J, Yun J, Lee J. Trajectory estimation of a moving object using Kalman filter and Kohonen networks. Robotica. 2007;25(5):567-574. DOI: 10.1017/s0263574707003451.

Hermes C, et al. Long-term vehicle motion prediction. 2009 IEEE Intelligent Vehicles Symposium. 2009;652-657. DOI: 10.1109/ivs.2009.5164354.

Chen H, Rakha HA. Prediction of dynamic freeway travel times based on vehicle trajectory construction. 2012 15th International IEEE Conference on Intelligent Transportation Systems. 2012. p. 576-581. DOI: 10.1109/itsc.2012.6338825.

Kim TG, Choi HT, Ko NY. Localization of a robot using particle filter with range and bearing information. 2013 10th International conference on ubiquitous robots and ambient intelligence (URAI). 2013. p. 368-370. DOI: 10.1109/urai.2013.6677389.

Zhang B, et al. Predicting vehicle trajectory via combination of model-based and data-driven methods using Kalman filter. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2023;09544070231161846. DOI: 10.1177/09544070231161846.

Farahi F, Yazdi HS. Probabilistic Kalman filter for moving object tracking. Signal Processing: Image Communication. 2020;82:115751. DOI: 10.1016/j.image.2019.115751.

Agrawal A, et al. Accurate prediction and estimation of 3d-repetitive-trajectories using Kalman filter, machine learning and curve-fitting method for high-speed target interception. Artificial Intelligence for Robotics and Autonomous Systems Applications. 2023;93-122. DOI: 10.1007/978-3-031-28715-2_4.

Lin CY, Kau LJ, Chan CY. Bimodal extended Kalman filter-based pedestrian trajectory prediction. Sensors. 2022;22(21):8231. DOI: 10.3390/s22218231.

Chen Y, Shu L, Wang L. Traffic flow prediction with big data: A deep learning based time series model. 2017 IEEE conference on Computer Communications Workshops (INFOCOM WKSHPS). 2017;1010-1011. DOI: 10.1109/infcomw.2017.8116535.

Chen Y, Hu C, Wang J. Human-centered trajectory tracking control for autonomous vehicles with driver cut-in behavior prediction. IEEE Transactions on Vehicular Technology. 2019;68(9):8461-8471. DOI: 10.1109/tvt.2019.2927242.

Dai S, Li L, Li Z. Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access. 2019;7:38287-38296. DOI: 10.1109/access.2019.2907000.

Lin L, et al. Long short-term memory-based human-driven vehicle longitudinal trajectory prediction in a connected and autonomous vehicle environment. Transportation Research Record. 2021;2675(6):380-390. DOI: 10.1177/0361198121993471.

Yang C, Pei Z. Long-short term spatio-temporal aggregation for trajectory prediction. IEEE Transactions on Intelligent Transportation Systems. 2023;24(4):4114-4126. DOI: 10.1109/tits.2023.3234962.

Fu E, et al. Temporal self-attention-based convlstm network for multivariate time series prediction. Neurocomputing. 2022;501:162-173. DOI: 10.1016/j.neucom.2022.06.014.

Xin L, et al. Intention-aware long horizon trajectory prediction of surrounding vehicles using dual LSTM networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). 2018. p. 1441-1446. DOI: 10.1109/itsc.2018.8569595.

Sajanraj T, et al. Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems. Neural Network World. 2021;(3). DOI: 10.14311/nnw.2021.31.009.

Liu J, Wang Z, Xu M. Deepmtt: A deep learning maneuvering targettracking algorithm based on bidirectional LSTM network. Information Fusion. 2020;53:289-304. DOI: 10.1016/j.inffus.2019.06.012.

Yu W, et al. Deepgtt: A general trajectory tracking deep learning algorithm based on dynamic law learning. IET Radar, Sonar & Navigation. 2021;15(9):1125-1150. DOI: 10.1049/rsn2.12111.

Siami-Namini S, Tavakoli N, Namin AS. The performance of LSTM and BiLSTM in forecasting time series. 2019 IEEE International conference on big data (Big Data). 2019. p. 3285-3292. DOI: 10.1109/bigdata47090.2019.9005997.

Yin J, Ning C, Tang T. Data-driven models for train control dynamics in high-speed railways: Lag-LSTM for train trajectory prediction. Information Sciences. 2022;600:377-400. DOI: 10.1016/j.ins.2022.04.004.

Yang CH, et al. Ais-based intelligent vessel trajectory prediction using Bi-LSTM. IEEE Access. 2022;10:24302-24315. DOI: 10.1109/access.2022.3154812.

Lin L, et al. Vehicle trajectory prediction using LSTMs with spatial-temporal attention mechanisms. IEEE Intelligent Transportation Systems Magazine. 2021;14(2):197-208. DOI: 10.1109/mits.2021.3049404.

Jaderberg M, et al. Population based training of neural networks. arXiv preprint arXiv:1711.09846. 2017. DOI: 10.48550/arXiv.1711.09846.