Trajectory Prediction and Intention Recognition Based on CNN-GRU
Accurate trajectory prediction and intention recognition are essential for effective decision-making, as different intents manifest in distinct trajectory patterns. This study focuses on trajectory prediction and intention recognition for airborne moving targets by proposing an integrated approach t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10877792/ |
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| author | Jinghao Du Dongdong Lu Fei Li Ke Liu Xiaolan Qiu |
| author_facet | Jinghao Du Dongdong Lu Fei Li Ke Liu Xiaolan Qiu |
| author_sort | Jinghao Du |
| collection | DOAJ |
| description | Accurate trajectory prediction and intention recognition are essential for effective decision-making, as different intents manifest in distinct trajectory patterns. This study focuses on trajectory prediction and intention recognition for airborne moving targets by proposing an integrated approach that leverages trajectory data for both tasks. Separate models were developed for trajectory prediction and intention recognition, with the trajectory prediction outcomes subsequently applied to enhance the accuracy of intention recognition. The proposed multi-channel parallel GRU-CNN neural network combines the temporal analysis capabilities of GRU with the spatial feature extraction strengths of CNN through a weight allocation strategy. This model achieved a notable performance, with a Mean Absolute Error (MAE) not exceeding 6.877. Additionally, a trajectory sequence similarity assessment system was constructed by incorporating Dynamic Time Warping (DTW), Hausdorff Distance (HD), and rate of altitude change, facilitating the classification of trajectories with similar intents. The integration of trajectory prediction into intention recognition significantly improved accuracy, as demonstrated by experimental results showing an accuracy rate exceeding 82%. These findings validate the effectiveness of the proposed method and offer an innovative technical solution for intention recognition of airborne moving targets. |
| format | Article |
| id | doaj-art-39ab6df20f474e06b7f68fac64724e31 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-39ab6df20f474e06b7f68fac64724e312025-08-20T01:55:53ZengIEEEIEEE Access2169-35362025-01-0113269452695710.1109/ACCESS.2025.353993110877792Trajectory Prediction and Intention Recognition Based on CNN-GRUJinghao Du0https://orcid.org/0009-0004-1379-6412Dongdong Lu1https://orcid.org/0000-0001-5965-4779Fei Li2https://orcid.org/0009-0007-7418-9415Ke Liu3https://orcid.org/0000-0002-0245-6170Xiaolan Qiu4Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, ChinaSuzhou Aerospace Information Research Institute, Suzhou, ChinaChinese Academy of Sciences, Aerospace Information Research Institute, Beijing, ChinaSuzhou Aerospace Information Research Institute, Suzhou, ChinaChinese Academy of Sciences, Aerospace Information Research Institute, Beijing, ChinaAccurate trajectory prediction and intention recognition are essential for effective decision-making, as different intents manifest in distinct trajectory patterns. This study focuses on trajectory prediction and intention recognition for airborne moving targets by proposing an integrated approach that leverages trajectory data for both tasks. Separate models were developed for trajectory prediction and intention recognition, with the trajectory prediction outcomes subsequently applied to enhance the accuracy of intention recognition. The proposed multi-channel parallel GRU-CNN neural network combines the temporal analysis capabilities of GRU with the spatial feature extraction strengths of CNN through a weight allocation strategy. This model achieved a notable performance, with a Mean Absolute Error (MAE) not exceeding 6.877. Additionally, a trajectory sequence similarity assessment system was constructed by incorporating Dynamic Time Warping (DTW), Hausdorff Distance (HD), and rate of altitude change, facilitating the classification of trajectories with similar intents. The integration of trajectory prediction into intention recognition significantly improved accuracy, as demonstrated by experimental results showing an accuracy rate exceeding 82%. These findings validate the effectiveness of the proposed method and offer an innovative technical solution for intention recognition of airborne moving targets.https://ieeexplore.ieee.org/document/10877792/Intention recognitiontrajectory predictionconvolutional neural networksgated recurrent unitstrajectory similarity |
| spellingShingle | Jinghao Du Dongdong Lu Fei Li Ke Liu Xiaolan Qiu Trajectory Prediction and Intention Recognition Based on CNN-GRU IEEE Access Intention recognition trajectory prediction convolutional neural networks gated recurrent units trajectory similarity |
| title | Trajectory Prediction and Intention Recognition Based on CNN-GRU |
| title_full | Trajectory Prediction and Intention Recognition Based on CNN-GRU |
| title_fullStr | Trajectory Prediction and Intention Recognition Based on CNN-GRU |
| title_full_unstemmed | Trajectory Prediction and Intention Recognition Based on CNN-GRU |
| title_short | Trajectory Prediction and Intention Recognition Based on CNN-GRU |
| title_sort | trajectory prediction and intention recognition based on cnn gru |
| topic | Intention recognition trajectory prediction convolutional neural networks gated recurrent units trajectory similarity |
| url | https://ieeexplore.ieee.org/document/10877792/ |
| work_keys_str_mv | AT jinghaodu trajectorypredictionandintentionrecognitionbasedoncnngru AT dongdonglu trajectorypredictionandintentionrecognitionbasedoncnngru AT feili trajectorypredictionandintentionrecognitionbasedoncnngru AT keliu trajectorypredictionandintentionrecognitionbasedoncnngru AT xiaolanqiu trajectorypredictionandintentionrecognitionbasedoncnngru |