Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion
In complex traffic environments, autonomous vehicles must thoroughly analyze the motion direction, speed, and other information of surrounding traffic objects to accurately predict future trajectories. A network model based on spatio-temporal Transformer was proposed to address this issue. The frame...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024192/ |
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| _version_ | 1841537110899687424 |
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| author | ZHAO Wenhong WANG Wei WAN Zilu |
| author_facet | ZHAO Wenhong WANG Wei WAN Zilu |
| author_sort | ZHAO Wenhong |
| collection | DOAJ |
| description | In complex traffic environments, autonomous vehicles must thoroughly analyze the motion direction, speed, and other information of surrounding traffic objects to accurately predict future trajectories. A network model based on spatio-temporal Transformer was proposed to address this issue. The framework initially employs a spatial self-attention mechanism to capture the spatial interactions between vehicles at the same moment, achieving precise modeling of the spatial relationship interactivity among multiple vehicles. Subsequently, a temporal self-attention mechanism was utilized to extract the temporal dependencies between consecutive frames, thereby generating a set of spatiotemporal features that reflect the dynamic behavior of vehicles. These features were then fed into a decoder to predict the motion trajectories of vehicles over the next 5 s. The proposed model was trained and validated on the publicly available NGSIM dataset. Compared to other state-of-the-art schemes, our scheme demonstrates greater accuracy and precision in trajectory prediction over the subsequent 5 s. The long-term forecasting accuracy is increased by 14.6% compared to the advanced schemes. |
| format | Article |
| id | doaj-art-0934390b805a4033a03857dc712d28de |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-0934390b805a4033a03857dc712d28de2025-01-14T08:46:16ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-11-014526727679134259Vehicle trajectory prediction based on spatio-temporal Transformer feature fusionZHAO WenhongWANG WeiWAN ZiluIn complex traffic environments, autonomous vehicles must thoroughly analyze the motion direction, speed, and other information of surrounding traffic objects to accurately predict future trajectories. A network model based on spatio-temporal Transformer was proposed to address this issue. The framework initially employs a spatial self-attention mechanism to capture the spatial interactions between vehicles at the same moment, achieving precise modeling of the spatial relationship interactivity among multiple vehicles. Subsequently, a temporal self-attention mechanism was utilized to extract the temporal dependencies between consecutive frames, thereby generating a set of spatiotemporal features that reflect the dynamic behavior of vehicles. These features were then fed into a decoder to predict the motion trajectories of vehicles over the next 5 s. The proposed model was trained and validated on the publicly available NGSIM dataset. Compared to other state-of-the-art schemes, our scheme demonstrates greater accuracy and precision in trajectory prediction over the subsequent 5 s. The long-term forecasting accuracy is increased by 14.6% compared to the advanced schemes.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024192/autonomous drivingtrajectory predictionmulti-vehicle interactionTransformer |
| spellingShingle | ZHAO Wenhong WANG Wei WAN Zilu Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion Tongxin xuebao autonomous driving trajectory prediction multi-vehicle interaction Transformer |
| title | Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion |
| title_full | Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion |
| title_fullStr | Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion |
| title_full_unstemmed | Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion |
| title_short | Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion |
| title_sort | vehicle trajectory prediction based on spatio temporal transformer feature fusion |
| topic | autonomous driving trajectory prediction multi-vehicle interaction Transformer |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024192/ |
| work_keys_str_mv | AT zhaowenhong vehicletrajectorypredictionbasedonspatiotemporaltransformerfeaturefusion AT wangwei vehicletrajectorypredictionbasedonspatiotemporaltransformerfeaturefusion AT wanzilu vehicletrajectorypredictionbasedonspatiotemporaltransformerfeaturefusion |