MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916287427379200 |
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| author | Yang, Zhongyu Liu, Mai Xie, Jinluo Zhang, Yueming Shen, Chen Shao, Wei Jiao, Jichao Xing, Tengfei Hu, Runbo Xu, Pengfei |
| author_facet | Yang, Zhongyu Liu, Mai Xie, Jinluo Zhang, Yueming Shen, Chen Shao, Wei Jiao, Jichao Xing, Tengfei Hu, Runbo Xu, Pengfei |
| contents | Autonomous driving without high-definition (HD) maps demands a higher level of active scene understanding. In this competition, the organizers provided the multi-perspective camera images and standard-definition (SD) maps to explore the boundaries of scene reasoning capabilities. We found that most existing algorithms construct Bird's Eye View (BEV) features from these multi-perspective images and use multi-task heads to delineate road centerlines, boundary lines, pedestrian crossings, and other areas. However, these algorithms perform poorly at the far end of roads and struggle when the primary subject in the image is occluded. Therefore, in this competition, we not only used multi-perspective images as input but also incorporated SD maps to address this issue. We employed map encoder pre-training to enhance the network's geometric encoding capabilities and utilized YOLOX to improve traffic element detection precision. Additionally, for area detection, we innovatively introduced LDTR and auxiliary tasks to achieve higher precision. As a result, our final OLUS score is 0.58. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_10125 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report Yang, Zhongyu Liu, Mai Xie, Jinluo Zhang, Yueming Shen, Chen Shao, Wei Jiao, Jichao Xing, Tengfei Hu, Runbo Xu, Pengfei Computer Vision and Pattern Recognition Autonomous driving without high-definition (HD) maps demands a higher level of active scene understanding. In this competition, the organizers provided the multi-perspective camera images and standard-definition (SD) maps to explore the boundaries of scene reasoning capabilities. We found that most existing algorithms construct Bird's Eye View (BEV) features from these multi-perspective images and use multi-task heads to delineate road centerlines, boundary lines, pedestrian crossings, and other areas. However, these algorithms perform poorly at the far end of roads and struggle when the primary subject in the image is occluded. Therefore, in this competition, we not only used multi-perspective images as input but also incorporated SD maps to address this issue. We employed map encoder pre-training to enhance the network's geometric encoding capabilities and utilized YOLOX to improve traffic element detection precision. Additionally, for area detection, we innovatively introduced LDTR and auxiliary tasks to achieve higher precision. As a result, our final OLUS score is 0.58. |
| title | MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.10125 |