Understanding driving scenarios is crucial to realizing autonomous driving.
Previous works such as map learning and BEV lane detection neglect the
connection relationship between lane instances, and traffic elements detection
tasks usually neglect the relationship with lane lines. To address these
issues, the task is presented which includes 4 sub-tasks, the detection of
traffic elements, the detection of lane centerlines, reasoning connection
relationships among lanes, and reasoning assignment relationships between lanes
and traffic elements. We present Separated RoadTopoFormer to tackle the issues,
which is an end-to-end framework that detects lane centerline and traffic
elements with reasoning relationships among them. We optimize each module
separately to prevent interaction with each other and aggregate them together
with few finetunes. For two detection heads, we adopted a DETR-like
architecture to detect objects, and for the relationship head, we concat two
instance features from front detectors and feed them to the classifier to
obtain relationship probability. Our final submission achieves 0.445 OLS, which
is competitive in both sub-task and combined scores.

提出了一个包括四个子任务的任务，包括交通要素检测、车道中心线检测、车道之间的关系推理以及车道和交通要素之间的关系推理，提出了一个端到端框架，名为 Separated RoadTopoFormer，可以检测出车道中心线和交通要素，并实现它们之间的推理关系，其中采用了 DETR-like 的体系结构来检测对象