Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things

Liu, Ryan Wen; Liang, Maohan; Nie, Jiangtian; Lim, Wei Yang Bryan; Zhang, Yang; Guizani, Mohsen

doi:10.1109/tnse.2022.3140529

Cited by 113 publications

(48 citation statements)

References 70 publications

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“…6G connectivity allows tracking of every single goods item at every step of this process. A similar approach is also expected even for boats (autonomous shipping) (Marr, 2021;Liu et al, 2022).…”

Section: Transportationmentioning

confidence: 59%

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Yaacoub¹

2022

Preprint

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<p>This paper investigates the synergy between 6G and AI. It argues that they can unlock future horizons, by discussing how they can address future challenges in healthcare, transportation, virtual reality, education, resource management, robotics, in addition to public safety and warfare. However, these great opportunities come also with greater risk. Therefore, the paper provides an overview of the security risks and challenges, along with possible mitigation techniques. <br></p>

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Section: Transportationmentioning

confidence: 59%

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Yaacoub¹

2022

Preprint

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“…Network traffic prediction is based on data prediction, and models that perform well in the field of data prediction will also be applicable in the field of network traffic. To guarantee high-accuracy vessel trajectory prediction, [12] proposes an AIS data-driven trajectory prediction framework, whose main component is a long short-term memory network. The vessel traffic conflict situation modeling, generated using the dynamic AIS data and social force concept, is embedded into the LSTM network.…”

Section: A Lstmmentioning

confidence: 99%

Network Traffic Prediction Based on LSTM and Transfer Learning

Wan

Liu

et al. 2022

IEEE Access

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The increasing amount of traffic in recent years has led to increasingly complex network problems. To be able to improve overall network performance and increase network utilization, it is valuable to take measures to capture future trends in network traffic. In traditional machine learning, to guarantee the accuracy and high reliability of the models obtained through training, there are two basic assumptions: (1) the training samples used for learning and the new test samples satisfy the condition of independent identical distribution; and (2) there must be enough training samples to learn a good model. However, time-series data are not easily accessible in real life, and even after putting in a lot of time and effort to collect them, the data may be unavailable due to confidentiality. In this paper, a neural network model based on LSTM and transfer learning is proposed to address the problem of small sample size in network traffic prediction. Knowledge in the source domain is transferred to the target domain using transfer learning, and a prediction model with good performance is constructed with a small amount of target domain data. The results show that the performance of the transfer learning model improves by more than 40% over the direct training model when using the same samples for predicting 10,000 rows of data, resulting in better performance of the network traffic prediction task.INDEX TERMS LSTM, network traffic prediction, transfer learning.

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“…Gupta et al [30] proposed Social-GAN to overcome the limitations of Social LSTM by introducing generative adversarial networks and the global pooling mechanism. In the above methods, RNN and its variants have become an important part of many recent trajectory prediction models [32,33] due to its powerful processing capability for time series data. However, RNN and its variants cannot be computed in parallel due to its own order structure, and has poor ability to extract the long-term dependence.…”

Section: Related Workmentioning

confidence: 99%

Multi-granularity scenarios understanding network for trajectory prediction

Yang

et al. 2022

Complex Intell. Syst.

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Understanding agents’ motion behaviors under complex scenes is crucial for intelligent autonomous moving systems (like delivery robots and self-driving cars). It is challenging duo to the inherent uncertain of future trajectories and the large variation in the scene layout. However, most recent approaches ignored or underutilized the scenario information. In this work, a Multi-Granularity Scenarios Understanding framework, MGSU, is proposed to explore the scene layout from different granularity. MGSU can be divided into three modules: (1) A coarse-grained fusion module uses the cross-attention to fuse the observed trajectory with the semantic information of the scene. (2) The inverse reinforcement learning module generates optimal path strategy through grid-based policy sampling and outputs multiple scene paths. (3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.

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Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things

Cited by 113 publications

References 70 publications

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Network Traffic Prediction Based on LSTM and Transfer Learning

Multi-granularity scenarios understanding network for trajectory prediction

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