A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Ma, Jian; Jia, Chengfeng; Yang, Xiaoxia; Cheng, Xiaochun; Li, Wenkai; Zhang, Chunwei

doi:10.1109/access.2020.3031722

Cited by 34 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 lists the "one to many" method of binary classification based on the classical matrix. The average accuracy, average accuracy, average recall, average Kappa coefficient, F1, and area AUC are calculated to appraise the classification performance of the two BILSTM models [34], as follows:…”

Section: Model Evaluationmentioning

confidence: 99%

Basic direction and realization path of PE teaching innovation in PSS based on deep learning model

Ke¹,

Wang²

2023

3C Tecnología

View full text Add to dashboard Cite

At present, the traditional model of PE in PSS (PSS)has seriously affected the quality of PE teaching in PSS and the perception of PE among primary and secondary school students. Because of the urgent need for innovation in PE in PSS, this study proposes the LSTM model to achieve an accurate prediction of the innovation direction of PE in PSS. Based on the LSTM model, the user behavior is classified by extracting the important features of the innovation direction. Expression to achieve accurate prediction of the future development direction of PE. Using the data confusion matrix to estimate the prediction accuracy of the LSTM model, the four evaluation indicators of Accuracy, Precision, F1, and AUC are 0.0532~0.2323 higher than the baseline model. The prediction results of PE teaching innovation in PSS from three aspects of teaching thought, teaching content, teaching objectives and essence are output, which has obvious guiding significance for the overall optimization of PE classrooms in PSS. This result shows that the LSTM prediction model has important practical value.

show abstract

Section: Model Evaluationmentioning

confidence: 99%

Basic direction and realization path of PE teaching innovation in PSS based on deep learning model

Ke¹,

Wang²

2023

3C Tecnología

View full text Add to dashboard Cite

show abstract

“…In 2019, A longterm trajectory motion prediction method based on a combined trajectory classification and LSTM network framework was proposed in [21]. Jie et al [33] in 2020 implemented a bi-directional long-short-term memory (BiLSTM) method to obtain the temporal and spatial dependence of behavior and its influence on future risks.…”

Section: B the Non-linear Modelsmentioning

confidence: 99%

FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network

Chen¹,

Li²,

Ye³

et al. 2022

Preprint

View full text Add to dashboard Cite

In order to improve the vessel's capacity and ensure maritime traffic safety, vessel intelligent trajectory prediction plays an essential role in the vessel's smart navigation and intelligent collision avoidance system. However, current researchers only focus on short-term or long-term vessel trajectory prediction, which leads to insufficient accuracy of trajectory prediction and lack of in-depth mining of comprehensive historical trajectory data. This paper proposes an Automatic Identification System (AIS) data-driven long short-term memory (LSTM) method based on the fusion of the forward sub-network and the reverse sub-network (termed as FRA-LSTM) to predict the vessel trajectory. The forward sub-network in our method combines LSTM and attention mechanism to mine features of forward historical trajectory data. Simultaneously, the reverse subnetwork combines bi-directional LSTM (BiLSTM) and attention mechanism to mine features of backward historical trajectory data. Finally, the final predicted trajectory is generated by fusing output features of the forward and reverse sub-network. Based on plenty of experiments, we prove that the accuracy of our proposed method in predicting short-term and mid-term trajectories has increased by 96.8% and 86.5% on average compared with the BiLSTM and Seq2seq. Furthermore, the average accuracy of our method is 90.1% higher than that of compared the BiLSTM and Seq2seq in predicting long-term trajectories.

show abstract

“…For this case, attention mechanisms provide a more appropriate solution (Luong et al, 2015 ). Several researchers have introduced the attention mechanism in the trajectory prediction model (Ma et al, 2020 ; Liang et al, 2022 ; Liu et al, 2022 ). Another group of researchers used attention mechanisms for feature extraction in sequential prediction.…”

Section: Introductionmentioning

confidence: 99%

End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism

Zhao,

Zuo,

Zhang

et al. 2024

Front. Comput. Neurosci.

View full text Add to dashboard Cite

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.

show abstract

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Cited by 34 publications

References 36 publications

Basic direction and realization path of PE teaching innovation in PSS based on deep learning model

Basic direction and realization path of PE teaching innovation in PSS based on deep learning model

FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network

End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism

Contact Info

Product

Resources

About