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.

翻译：驾驶场景理解任务涉及检测静态元素，如车道、交通标志和交通信号灯，以及它们之间的相互关系。为促进利用多视角摄像头开发全面的场景理解解决方案，研究人员发布了名为Road Genome（OpenLane-V2）的新数据集。该数据集支持探索复杂的道路连接以及车道标线可能缺失的场景。不同于传统的车道标线，该数据集中的车道通过中心线表示，这种表示方式更适用于描述车道及其连接关系。在本研究中，我们提出了一种名为TopoMask的新方法，用于预测道路拓扑中的中心线。与现有文献中依赖关键点或参数化方法的方式不同，TopoMask采用基于实例掩码的公式化方法，结合Transformer架构，并引入方向标签表示以丰富掩码实例的流向信息。在OpenLane拓扑挑战赛2023中，TopoMask在OpenLane-V2评分（OLS）中排名第四，在中心线预测的F1分数中排名第二。与当前最先进方法TopoNet相比，所提方法在基于弗雷歇距离的车道检测中取得了相似性能，且在基于倒角距离的车道检测中优于TopoNet，且无需使用其场景图神经网络。