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.

自动驾驶无需高清地图，但需要更高水平的场景理解能力。在这项竞赛中，使用多角度相机图像和标清地图来探索场景推理能力的边界。通过加入地图编码预训练和使用 YOLOX 等算法，我们对交通要素进行了更精准的检测，最终实现了 0.58 的 OLUS 评分。