Trajectory Prediction for Autonomous Driving Using a Transformer Network
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929256723906560 |
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| author | Li, Zhenning Yu, Hao |
| author_facet | Li, Zhenning Yu, Hao |
| contents | Predicting the trajectories of surrounding agents is still considered one of the most challenging tasks for autonomous driving. In this paper, we introduce a multi-modal trajectory prediction framework based on the transformer network. The semantic maps of each agent are used as inputs to convolutional networks to automatically derive relevant contextual information. A novel auxiliary loss that penalizes unfeasible off-road predictions is also proposed in this study. Experiments on the Lyft l5kit dataset show that the proposed model achieves state-of-the-art performance, substantially improving the accuracy and feasibility of the prediction outcomes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_16501 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trajectory Prediction for Autonomous Driving Using a Transformer Network Li, Zhenning Yu, Hao Robotics Predicting the trajectories of surrounding agents is still considered one of the most challenging tasks for autonomous driving. In this paper, we introduce a multi-modal trajectory prediction framework based on the transformer network. The semantic maps of each agent are used as inputs to convolutional networks to automatically derive relevant contextual information. A novel auxiliary loss that penalizes unfeasible off-road predictions is also proposed in this study. Experiments on the Lyft l5kit dataset show that the proposed model achieves state-of-the-art performance, substantially improving the accuracy and feasibility of the prediction outcomes. |
| title | Trajectory Prediction for Autonomous Driving Using a Transformer Network |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.16501 |