Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information
A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. The algorithm analyzes the ch...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/4736623 |
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| _version_ | 1849403856715251712 |
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| author | Ruibin Zhang Yingshi Guo Yunze Long Yang Zhou Chunyan Jiang |
| author_facet | Ruibin Zhang Yingshi Guo Yunze Long Yang Zhou Chunyan Jiang |
| author_sort | Ruibin Zhang |
| collection | DOAJ |
| description | A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. The algorithm analyzes the characteristics of object motion that are affected by the surrounding environment and the interaction of nearby objects and is based on the complex traffic environment perception dual multiline light detection and ranging (LiDAR) technology. The time sequence aerial view map and time sequence front view depth map are obtained using real-time point cloud information perceived by the LiDAR. Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. A temporal feature vector is constructed as the input data of the bidirectional long-term and short-term memory (BiLSTM) network, and the desired input-output mapping relationship is trained to predict the motion state of traffic participants. According to the test results, the proposed BiLSTM model based on point cloud multiview and vehicle interaction information is better than other methods in predicting the state of target vehicles. The results can provide support for the research to evaluate the risk of intelligent vehicle operation environment. |
| format | Article |
| id | doaj-art-97b399871b5e429e95b2db8acfca3551 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-97b399871b5e429e95b2db8acfca35512025-08-20T03:37:09ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/4736623Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive InformationRuibin Zhang0Yingshi Guo1Yunze Long2Yang Zhou3Chunyan Jiang4School of AutomobileSchool of AutomobileSchool of Automobile EngineeringSchool of AutomobileSchool of Automobile EngineeringA vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. The algorithm analyzes the characteristics of object motion that are affected by the surrounding environment and the interaction of nearby objects and is based on the complex traffic environment perception dual multiline light detection and ranging (LiDAR) technology. The time sequence aerial view map and time sequence front view depth map are obtained using real-time point cloud information perceived by the LiDAR. Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. A temporal feature vector is constructed as the input data of the bidirectional long-term and short-term memory (BiLSTM) network, and the desired input-output mapping relationship is trained to predict the motion state of traffic participants. According to the test results, the proposed BiLSTM model based on point cloud multiview and vehicle interaction information is better than other methods in predicting the state of target vehicles. The results can provide support for the research to evaluate the risk of intelligent vehicle operation environment.http://dx.doi.org/10.1155/2022/4736623 |
| spellingShingle | Ruibin Zhang Yingshi Guo Yunze Long Yang Zhou Chunyan Jiang Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information Journal of Advanced Transportation |
| title | Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information |
| title_full | Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information |
| title_fullStr | Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information |
| title_full_unstemmed | Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information |
| title_short | Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information |
| title_sort | vehicle motion state prediction method integrating point cloud time series multiview features and multitarget interactive information |
| url | http://dx.doi.org/10.1155/2022/4736623 |
| work_keys_str_mv | AT ruibinzhang vehiclemotionstatepredictionmethodintegratingpointcloudtimeseriesmultiviewfeaturesandmultitargetinteractiveinformation AT yingshiguo vehiclemotionstatepredictionmethodintegratingpointcloudtimeseriesmultiviewfeaturesandmultitargetinteractiveinformation AT yunzelong vehiclemotionstatepredictionmethodintegratingpointcloudtimeseriesmultiviewfeaturesandmultitargetinteractiveinformation AT yangzhou vehiclemotionstatepredictionmethodintegratingpointcloudtimeseriesmultiviewfeaturesandmultitargetinteractiveinformation AT chunyanjiang vehiclemotionstatepredictionmethodintegratingpointcloudtimeseriesmultiviewfeaturesandmultitargetinteractiveinformation |