Online Vectorized HD Map Construction using Geometry
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯AI实验室）
The construction of online vectorized High-Definition (HD) maps is critical for downstream prediction and planning. Recent efforts have built strong baselines for this task, however, shapes and relations of instances in urban road systems are still under-explored, such as parallelism, perpendicular, or rectangle-shape. In our work, we propose GeMap ($\textbf{Ge}$ometry $\textbf{Map}$), which end-to-end learns Euclidean shapes and relations of map instances beyond basic perception. Specifically, we design a geometric loss based on angle and distance clues, which is robust to rigid transformations. We also decouple self-attention to independently handle Euclidean shapes and relations. Our method achieves new state-of-the-art performance on the NuScenes and Argoverse 2 datasets. Remarkably, it reaches a 71.8% mAP on the large-scale Argoverse 2 dataset, outperforming MapTR V2 by +4.4% and surpassing the 70% mAP threshold for the first time. Code is available at https://github.com/cnzzx/GeMap.
MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving
Mercedes-Benz Research & Development North America（梅赛德斯-奔驰北美研发有限公司）；University of Stuttgart（斯图加特大学）；Institute for Artificial Intelligence（人工智能研究所）；University of California, San Diego（加州大学圣地亚哥分校）；University of Southampton（南安普顿大学）
Huazhong University of Science & Technology（华中科技大学）；Horizon Robotics（地平线机器人）
High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. We present MapTR, a structured end-to-end Transformer for efficient online vectorized HD map construction. We propose a unified permutation-equivalent modeling approach, i.e., modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process. We design a hierarchical query embedding scheme to flexibly…
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Horizon Robotics（地平线机器人）
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
Mercedes-Benz Research & Development North America, USA（梅赛德斯-奔驰北美研究与开发）；University of Stuttgart, Germany（斯图加特大学）；University of California, San Diego, USA（加州大学圣地亚哥分校）；University of Southampton, United Kingdom（南安普顿大学）
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；National Engineering Research Center of Optical Instrumentation, Zhejiang University（浙江大学光学仪器国家工程研究中心）；College of Computer Science, Zhejiang University（浙江大学计算机学院）；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人形机器人与机器人学研究所）；USC Viterbi School of Engineering, the University of Southern California（南加州大学维特比工程学院）
Tsinghua University（清华大学）；Massachusetts Institute of Technology（麻省理工学院）；Li Auto（理想汽车）
Constructing HD semantic maps is a central component of autonomous driving. However, traditional pipelines require a vast amount of human efforts and resources in annotating and maintaining the semantics in the map, which limits its scalability. In this paper, we introduce the problem of HD semantic map learning, which dynamically constructs the local semantics based on onboard sensor observations. Meanwhile, we introduce a semantic map learning method, dubbed HDMapNet. HDMapNet encodes image features from surrounding cameras and/or point clouds from LiDAR, and predicts vectorized map elements…
MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert
Lanzhou University（兰州大学）；Smart (Shanghai) Robotics Technology Co., Ltd.（智己（上海）机器人科技有限公司）；University of Science and Technology of China（中国科学技术大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Online3R: Online Learning for Consistent Sequential Reconstruction Based on Geometry Foundation Model
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；T-Stone Robotics Institute, The Chinese University of Hong Kong（香港中文大学天石机器人研究所）；NVIDIA（英伟达）；College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；School of Artificial Intelligence and Computer Science, Anqing Normal University（安庆师范大学人工智能与计算机科学学院）
Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps
Bosch North America（博世北美）；Bosch Center for AI (BCAI)（博世人工智能中心）；Robert Bosch GmbH（罗伯特·博世有限公司）；Contextual Robotics Institute（情境机器人学研究所）；UC San Diego（加州大学圣迭戈分校）
