Driving scene understanding task involves detecting static elements such as
lanes, traffic signs, and traffic lights, and their relationships with each
other. To facilitate the development of comprehensive scene understanding
solutions using multiple camera views, a new dataset called Road Genome
(OpenLane-V2) has been released. This dataset allows for the exploration of
complex road connections and situations where lane markings may be absent.
Instead of using traditional lane markings, the lanes in this dataset are
represented by centerlines, which offer a more suitable representation of lanes
and their connections. In this study, we have introduced a new approach called
TopoMask for predicting centerlines in road topology. Unlike existing
approaches in the literature that rely on keypoints or parametric methods,
TopoMask utilizes an instance-mask based formulation with a transformer-based
architecture and, in order to enrich the mask instances with flow information,
a direction label representation is proposed. TopoMask have ranked 4th in the
OpenLane-V2 Score (OLS) and ranked 2nd in the F1 score of centerline prediction
in OpenLane Topology Challenge 2023. In comparison to the current
state-of-the-art method, TopoNet, the proposed method has achieved similar
performance in Frechet-based lane detection and outperformed TopoNet in
Chamfer-based lane detection without utilizing its scene graph neural network.

本研究提出了一种基于实例掩模和变形器的中心线预测方法，利用方向标签表示来丰富掩模实例的流信息，该方法在 OpenLane-V2 得分（OLS）中排名第四，在 OpenLane 拓扑挑战的中心线预测的 F1 得分中排名第二，并在不使用其情境图神经网络的情况下在 Frechet-based 车道检测方面显示出类似的性能，并在 Chamfer-based 车道检测方面优于 TopoNet 方法。