Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.). Previous work struggled in complex cases due to their simple designs of the spatial transformation between front view and bird's eye view (BEV) and the lack of a realistic dataset. Towards these issues, we present PersFormer: an end-to-end monocular 3D lane detector with a novel Transformer-based spatial feature transformation module. Our model generates BEV features by attending to related front-view local regions with camera parameters as a reference. PersFormer adopts a unified 2D/3D anchor design and an auxiliary task to detect 2D/3D lanes simultaneously, enhancing the feature consistency and sharing the benefits of multi-task learning. Moreover, we release one of the first large-scale real-world 3D lane datasets: OpenLane, with high-quality annotation and scenario diversity. OpenLane contains 200,000 frames, over 880,000 instance-level lanes, 14 lane categories, along with scene tags and the closed-in-path object annotations to encourage the development of lane detection and more industrial-related autonomous driving methods. We show that PersFormer significantly outperforms competitive baselines in the 3D lane detection task on our new OpenLane dataset as well as Apollo 3D Lane Synthetic dataset, and is also on par with state-of-the-art algorithms in the 2D task on OpenLane. The project page is available at https://github.com/OpenPerceptionX/PersFormer_3DLane and OpenLane dataset is provided at https://github.com/OpenPerceptionX/OpenLane.
Beijing Institute of Technology（北京理工大学）；MEGVII Technology（旷视科技）；University of Science and Technology of China（中国科学技术大学）；SKL-IOTSC, University of Macau（澳门大学智慧城市物联网国家重点实验室）
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 h…
Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；PhiGent Robotics（PhiGent机器人公司）；UCAS（中国科学院大学）；Luoyang Institute for Robot and Intelligent Equipment（洛阳机器人与智能装备研究院）
3D lane detection which plays a crucial role in vehicle routing, has recently been a rapidly developing topic in autonomous driving. Previous works struggle with practicality due to their complicated spatial transformations and inflexible representations of 3D lanes. Faced with the issues, our work proposes an efficient and robust monocular 3D lane detection called BEV-LaneDet with three main contributions. First, we introduce the Virtual Camera that unifies the in/extrinsic parameters of cameras mounted on different vehicles to guarantee the consistency of the spatial relationship among camer…
RATopo: Improving Lane Topology Reasoning via Redundancy Assignment
School of Artificial Intelligence Beihang University Beijing China（人工智能学院 北航 北京 中国）；Zhongguancun Academy Beijing China（中关村学院 北京 中国）；Hangzhou International Innovation Institute Beihang University Hangzhou China（杭州国际创新研究院 北航 杭州 中国）；Beihang University（北航）；Zhongguancun Academy（中关村学院）；University of Macau（澳门大学）
TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
Institute of Computing Technology, Chinese Academy of Science（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai AI Lab（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）
Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Bosch Corporate Research, China（博世中国企业研究中心）；Institute for Interdisciplinary Information Sciences(IIIS), Tsinghua University（清华大学交叉信息研究院）
Reusing Attention for One-stage Lane Topology Understanding
Institute for AI Industry Research, Tsinghua University（人工智能产业研究院，清华大学）；Bosch Corporate Research, China（博世企业研究，中国）；Institute for Interdisciplinary Information Sciences, Tsinghua University（交叉信息科学研究院，清华大学）；Department of Computer Science, ETH Zürich（计算机科学系，苏黎世联邦理工学院）；State Key Lab of Intelligent Transportation System, Beihang University（智能交通系统国家重点实验室，北航）
OpenDriveLab（开放驾驶实验室）；Shanghai AI Lab（上海人工智能实验室）；Huawei（华为）；Fudan University（复旦大学）；The University of Hong Kong（香港大学）；Shanghai Jiao Tong University（上海交通大学）
TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hangzhou Dianzi University（杭州电子科技大学）
A Concise Survey on Lane Topology Reasoning for HD Mapping
NavInfo Co., Ltd.（四维图新有限公司）；Autohome Inc.（汽车之家）；Baidu Inc.（百度公司）；Xidian University of Technology（西安工业大学）；Hefei University of Technology（合肥工业大学）；Renmin University of China（中国人民大学）
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
Institute of Artificial Intelligence, Huazhong University of Science & Technology（华中科技大学人工智能研究院）；School of EIC, Huazhong University of Science & Technology（华中科技大学电子信息与通信学院）；Horizon Robotics（地平线机器人）
