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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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1000-436X |