This research work seeks to explore and identify strategies that can
determine road topology information in 2D and 3D under highly dynamic urban
driving scenarios. To facilitate this exploration, we introduce a substantial
dataset comprising nearly one million automatically labeled data frames. A key
contribution of our research lies in developing an automatic label-generation
process and an occlusion handling strategy. This strategy is designed to model
a wide range of occlusion scenarios, from mild disruptions to severe blockages.
Furthermore, we present a comprehensive ablation study wherein multiple
centerline detection methods are developed and evaluated. This analysis not
only benchmarks the performance of various approaches but also provides
valuable insights into the interpretability of these methods. Finally, we
demonstrate the practicality of our methods and assess their adaptability
across different sensor configurations, highlighting their versatility and
relevance in real-world scenarios. Our dataset and experimental models are
publicly available.

本研究旨在探索和确定在高度动态的城市驾驶场景下确定 2D 和 3D 的道路拓扑信息的策略。为了促进这一探索，我们引入了一个庞大的数据集，包含近 100 万个自动标记的数据帧。我们的研究的一个重要贡献在于开发了一个自动生成标签的过程和一个遮挡处理策略，该策略旨在模拟从轻微的中断到严重的阻塞的各种遮挡场景。此外，我们进行了全面的消融研究，开发和评估了多种中心线检测方法。这种分析不仅评估了各种方法的性能，还提供了宝贵的对这些方法可解释性的见解。最后，我们演示了我们的方法的实用性，并评估了它们在不同传感器配置下的适应性，突出了它们在实际场景中的多功能性和相关性。我们的数据集和实验模型是公开可用的。

自动标记生成的城市驾驶遮挡感知 2D 和 3D 中心线检测

Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via  Automatic Label Generation

We present the 1st-place solution of OpenLane Topology in Autonomous Driving
Challenge. Considering that topology reasoning is based on centerline detection
and traffic element detection, we develop a multi-stage framework for high
performance. Specifically, the centerline is detected by the powerful PETRv2
detector and the popular YOLOv8 is employed to detect the traffic elements.
Further, we design a simple yet effective MLP-based head for topology
prediction. Our method achieves 55\% OLS on the OpenLaneV2 test set, surpassing
the 2nd solution by 8 points.

本篇论文提出了一种用于自动驾驶的拓扑解决方案，其中采用了多阶段框架来实现高性能，包括使用 PETRv2 探测器检测中心线，使用 YOLOv8 检测交通要素，设计简单而有效的基于 MLP 的拓扑预测头， 其方法在 OpenLaneV2 测试集上实现了 55％的 OLS，超过第二个解决方案 8 个点。