Bidirectional RNN-based private car trajectory reconstruction algorithm
To address the problem that in the complex urban environment, due to the inevitable interruption of GNSS positioning signal and the accumulation of errors during vehicle driving, the collected vehicle trajectory data was likely to be inaccurate and incomplete.a bidirectional weighted trajectory reco...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2020-12-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020227/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539290762313728 |
|---|---|
| author | Zhu XIAO Xin QIAN Hongbo JIANG Chenglin CAI Fanzi ZENG |
| author_facet | Zhu XIAO Xin QIAN Hongbo JIANG Chenglin CAI Fanzi ZENG |
| author_sort | Zhu XIAO |
| collection | DOAJ |
| description | To address the problem that in the complex urban environment, due to the inevitable interruption of GNSS positioning signal and the accumulation of errors during vehicle driving, the collected vehicle trajectory data was likely to be inaccurate and incomplete.a bidirectional weighted trajectory reconstruction algorithm was proposed based on RNN neural network.The GNSS-OBD trajectory acquisition device was used to collect vehicle trajectory information, and multi-source data fusion was adopted to achieve bidirectional weighted trajectory reconstruction.Furthermore, the neural arithmetic logic unit (NALU) was leveraged with the purpose of enhancing the extrapolation ability of deep network and ensuring the accuracy of trajectory reconstruction.For the evaluation, real-world experiments were conducted to evaluate the performance of the proposed method in comparison with existing methods.The root mean square error (RMSE) indicator shows the algorithm accuracy and the reconstructed trajectory is visually displayed through Google Earth.Experimental results validate the effectiveness and reliability of the proposed algorithm. |
| format | Article |
| id | doaj-art-8fd4add9eb684279b5843fde82963248 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8fd4add9eb684279b5843fde829632482025-01-14T07:21:23ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-12-014117118159739385Bidirectional RNN-based private car trajectory reconstruction algorithmZhu XIAOXin QIANHongbo JIANGChenglin CAIFanzi ZENGTo address the problem that in the complex urban environment, due to the inevitable interruption of GNSS positioning signal and the accumulation of errors during vehicle driving, the collected vehicle trajectory data was likely to be inaccurate and incomplete.a bidirectional weighted trajectory reconstruction algorithm was proposed based on RNN neural network.The GNSS-OBD trajectory acquisition device was used to collect vehicle trajectory information, and multi-source data fusion was adopted to achieve bidirectional weighted trajectory reconstruction.Furthermore, the neural arithmetic logic unit (NALU) was leveraged with the purpose of enhancing the extrapolation ability of deep network and ensuring the accuracy of trajectory reconstruction.For the evaluation, real-world experiments were conducted to evaluate the performance of the proposed method in comparison with existing methods.The root mean square error (RMSE) indicator shows the algorithm accuracy and the reconstructed trajectory is visually displayed through Google Earth.Experimental results validate the effectiveness and reliability of the proposed algorithm.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020227/private carvehicle positioningtrajectory reconstructionRNN |
| spellingShingle | Zhu XIAO Xin QIAN Hongbo JIANG Chenglin CAI Fanzi ZENG Bidirectional RNN-based private car trajectory reconstruction algorithm Tongxin xuebao private car vehicle positioning trajectory reconstruction RNN |
| title | Bidirectional RNN-based private car trajectory reconstruction algorithm |
| title_full | Bidirectional RNN-based private car trajectory reconstruction algorithm |
| title_fullStr | Bidirectional RNN-based private car trajectory reconstruction algorithm |
| title_full_unstemmed | Bidirectional RNN-based private car trajectory reconstruction algorithm |
| title_short | Bidirectional RNN-based private car trajectory reconstruction algorithm |
| title_sort | bidirectional rnn based private car trajectory reconstruction algorithm |
| topic | private car vehicle positioning trajectory reconstruction RNN |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020227/ |
| work_keys_str_mv | AT zhuxiao bidirectionalrnnbasedprivatecartrajectoryreconstructionalgorithm AT xinqian bidirectionalrnnbasedprivatecartrajectoryreconstructionalgorithm AT hongbojiang bidirectionalrnnbasedprivatecartrajectoryreconstructionalgorithm AT chenglincai bidirectionalrnnbasedprivatecartrajectoryreconstructionalgorithm AT fanzizeng bidirectionalrnnbasedprivatecartrajectoryreconstructionalgorithm |