MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909094188679168 |
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| author | Gan, Yiqian Xiao, Hao Zhao, Yizhe Zhang, Ethan Huang, Zhe Ye, Xin Ge, Lingting |
| author_facet | Gan, Yiqian Xiao, Hao Zhao, Yizhe Zhang, Ethan Huang, Zhe Ye, Xin Ge, Lingting |
| contents | Motion prediction has been an essential component of autonomous driving systems since it handles highly uncertain and complex scenarios involving moving agents of different types. In this paper, we propose a Multi-Granular TRansformer (MGTR) framework, an encoder-decoder network that exploits context features in different granularities for different kinds of traffic agents. To further enhance MGTR's capabilities, we leverage LiDAR point cloud data by incorporating LiDAR semantic features from an off-the-shelf LiDAR feature extractor. We evaluate MGTR on Waymo Open Dataset motion prediction benchmark and show that the proposed method achieved state-of-the-art performance, ranking 1st on its leaderboard (https://waymo.com/open/challenges/2023/motion-prediction/). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02409 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR Gan, Yiqian Xiao, Hao Zhao, Yizhe Zhang, Ethan Huang, Zhe Ye, Xin Ge, Lingting Computer Vision and Pattern Recognition Robotics Motion prediction has been an essential component of autonomous driving systems since it handles highly uncertain and complex scenarios involving moving agents of different types. In this paper, we propose a Multi-Granular TRansformer (MGTR) framework, an encoder-decoder network that exploits context features in different granularities for different kinds of traffic agents. To further enhance MGTR's capabilities, we leverage LiDAR point cloud data by incorporating LiDAR semantic features from an off-the-shelf LiDAR feature extractor. We evaluate MGTR on Waymo Open Dataset motion prediction benchmark and show that the proposed method achieved state-of-the-art performance, ranking 1st on its leaderboard (https://waymo.com/open/challenges/2023/motion-prediction/). |
| title | MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2312.02409 |