Separated RoadTopoFormer
Mingjie Lu Yuanxian Huang Ji Liu Jinzhang Peng Lu Tian Ashish Sirasao
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.