Autonomous driving without high-definition (HD) maps demands a higher level
of active scene understanding. In this competition, the organizers provided the
multi-perspective camera images and standard-definition (SD) maps to explore
the boundaries of scene reasoning capabilities. We found that most existing
algorithms construct Bird's Eye View (BEV) features from these
multi-perspective images and use multi-task heads to delineate road
centerlines, boundary lines, pedestrian crossings, and other areas. However,
these algorithms perform poorly at the far end of roads and struggle when the
primary subject in the image is occluded. Therefore, in this competition, we
not only used multi-perspective images as input but also incorporated SD maps
to address this issue. We employed map encoder pre-training to enhance the
network's geometric encoding capabilities and utilized YOLOX to improve traffic
element detection precision. Additionally, for area detection, we innovatively
introduced LDTR and auxiliary tasks to achieve higher precision. As a result,
our final OLUS score is 0.58.

自动驾驶无需高清地图，但需要更高水平的场景理解能力。在这项竞赛中，使用多角度相机图像和标清地图来探索场景推理能力的边界。通过加入地图编码预训练和使用 YOLOX 等算法，我们对交通要素进行了更精准的检测，最终实现了 0.58 的 OLUS 评分。

MapVision: CVPR 2024 自主大挑战无地图驾驶技术报告

MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech  Report

Topology reasoning aims to comprehensively understand road scenes and present
drivable routes in autonomous driving. It requires detecting road centerlines
(lane) and traffic elements, further reasoning their topology relationship,
i.e., lane-lane topology, and lane-traffic topology. In this work, we first
present that the topology score relies heavily on detection performance on lane
and traffic elements. Therefore, we introduce a powerful 3D lane detector and
an improved 2D traffic element detector to extend the upper limit of topology
performance. Further, we propose TopoMLP, a simple yet high-performance
pipeline for driving topology reasoning. Based on the impressive detection
performance, we develop two simple MLP-based heads for topology generation.
TopoMLP achieves state-of-the-art performance on OpenLane-V2 benchmark, i.e.,
41.2% OLS with ResNet-50 backbone. It is also the 1st solution for 1st OpenLane
Topology in Autonomous Driving Challenge. We hope such simple and strong
pipeline can provide some new insights to the community. Code is at
this https URL

我们提出了 TopoMLP，这是一个简单但高性能的驾驶拓扑推理管道，基于令人印象深刻的检测性能，我们为拓扑生成开发了两个基于 MLP 的简单头部。

TopoMLP: 一个简单而强大的用于驱动拓扑推理的流程

TopoMLP: An Simple yet Strong Pipeline for Driving Topology Reasoning

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。