分离式道路拓扑Transformer（Separated RoadTopoFormer）
Separated RoadTopoFormer
AI总结：
本文提出分离式道路拓扑Transformer，通过端到端框架分别优化车道中心线检测、交通元素检测及关系推理模块，解决自动驾驶场景中车道与交通元素连接关系建模问题，最终取得0.445 OLS的竞争性成绩。
摘要：
理解驾驶场景对于实现自动驾驶至关重要。此前的工作，如地图学习和鸟瞰图（BEV）车道检测，忽略了车道实例之间的连接关系，而交通元素检测任务通常忽略了与车道线的关系。为了解决这些问题，提出了包含4个子任务的任务：交通元素检测、车道中心线检测、车道间连接关系推理，以及车道与交通元素之间分配关系推理。我们提出了分离式道路拓扑Transformer（Separated RoadTopoFormer）来解决这些问题，这是一个端到端框架，能够检测车道中心线和交通元素，并推理它们之间的关系。我们分别优化每个模块以防止相互干扰，并通过少量微调将它们聚合在一起。对于两个检测头，我们采用了类似DETR的架构来检测目标；对于关系头，我们将来自前端检测器的两个实例特征拼接起来，并输入分类器以获得关系概率。我们的最终提交取得了0.445的OLS分数，在子任务和综合分数上都具有竞争力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
