  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.
