An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones
Vehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising per...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/2661136 |
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| _version_ | 1832547909676040192 |
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| author | Qiucheng Chen Shunying Zhu Jingan Wu Hongguang Chang Hong Wang |
| author_facet | Qiucheng Chen Shunying Zhu Jingan Wu Hongguang Chang Hong Wang |
| author_sort | Qiucheng Chen |
| collection | DOAJ |
| description | Vehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising performance of the linear Kalman filter (KF). To address this issue, this study proposes a new denoising method based on the adaptive Kalman filter, which automatically switches between KF and Unscented KF to accommodate car-following and merging behaviours, respectively. A merging behaviour detection method was designed based on the PELT method and normalized innovation squared (NIS). The F1 score of 92.9% shows the accuracy of behaviour detection. According to our results, the proposed method minimizes the range of jerk compared with other methods, reducing it from −4927.78 to 4960.72 of raw data to −44.92 to 47.14, indicating a significant improvement in denoising and trajectory smoothing. The goal of this study is to achieve high-precision trajectory data under complex real traffic scenarios. |
| format | Article |
| id | doaj-art-3e39766c40844343b3a7e1e5a0d8bcac |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-3e39766c40844343b3a7e1e5a0d8bcac2025-02-03T06:42:56ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/2661136An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging ZonesQiucheng Chen0Shunying Zhu1Jingan Wu2Hongguang Chang3Hong Wang4Department of Traffic EngineeringDepartment of Traffic EngineeringDepartment of Traffic EngineeringDepartment of Traffic EngineeringDepartment of Road and Bridge EngineeringVehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising performance of the linear Kalman filter (KF). To address this issue, this study proposes a new denoising method based on the adaptive Kalman filter, which automatically switches between KF and Unscented KF to accommodate car-following and merging behaviours, respectively. A merging behaviour detection method was designed based on the PELT method and normalized innovation squared (NIS). The F1 score of 92.9% shows the accuracy of behaviour detection. According to our results, the proposed method minimizes the range of jerk compared with other methods, reducing it from −4927.78 to 4960.72 of raw data to −44.92 to 47.14, indicating a significant improvement in denoising and trajectory smoothing. The goal of this study is to achieve high-precision trajectory data under complex real traffic scenarios.http://dx.doi.org/10.1155/2023/2661136 |
| spellingShingle | Qiucheng Chen Shunying Zhu Jingan Wu Hongguang Chang Hong Wang An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones Journal of Advanced Transportation |
| title | An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones |
| title_full | An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones |
| title_fullStr | An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones |
| title_full_unstemmed | An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones |
| title_short | An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones |
| title_sort | acceleration denoising method based on an adaptive kalman filter for trajectory in merging zones |
| url | http://dx.doi.org/10.1155/2023/2661136 |
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