TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
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 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 https://github.com/wudongming97/TopoMLP.
The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge
MEGVII Technology（旷视科技）；Beijing Institute of Technology（北京理工大学）；University of Science and Technology of China（中国科学技术大学）；The University of Hong Kong（香港大学）
Refine Connections, Close the Gap: A Reliable Enhancement Framework for Driving Scene Topology
Bosch Research North America（博世北美研究院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心）；Massachusetts Institute of Technology（麻省理工学院）；Bosch XC China（博世中国XC部门）
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）
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）
TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations
Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences（智能机器研究所，HFIPS，中国科学院）；University of Science and Technology of China（中国科学技术大学）；NullMax；Westlake University（西湖大学）
The majority of current approaches in autonomous driving rely on High-Definition (HD) maps which detail the road geometry and surrounding area. Yet, this reliance is one of the obstacles to mass deployment of autonomous vehicles due to poor scalability of such prior maps. In this paper, we tackle the problem of online road map extraction via leveraging the sensory system aboard the vehicle itself. To this end, we design a structured model where a graph representation of the road network is generated in a hierarchical fashion within a fully convolutional network. The method is able to handle co…
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）
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；Zhuoyu Technology（卓予科技）；State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）
Uber Advanced Technologies Group（优步先进技术集团）；University of Toronto（多伦多大学）；MIT（麻省理工学院）
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost. Current attempts to automate this process typically focus on simple scenarios, estimate independent maps per frame or do not have the level of precision required by modern self driving vehicles. In contrast, in this paper we focus on drawing the lane boundaries of complex highways with many lanes that contain topology changes due to forks and merges. Towards this goal, we formulate the problem as inference in a directed acyclic graphical model (DAG), where the …
CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving
the Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong, China（电子与计算机工程系，香港科技大学，香港，中国）；the Academy for Engineering and Technology, Fudan University, Shanghai, China（工程与技术学院，复旦大学，上海，中国）；Zhuoyu Technology Co., Ltd., Shenzhen, China（珠海优创科技有限公司，深圳，中国）
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）
Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furthermore, it needs to refer to the ground plane, where also the driving actions are taken. This paper aims at extracting the local road network topology, directly in the bird's-eye-view (BEV), all in a complex urban setting. The only input consists of a single onboard, forward looking camera image. We represent the road topology using a set of directed lane curves and their interactions, which are captured using their int…
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）
ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data
Mercedes-Benz AG, Research \& Development, Sindelfingen, Germany Institute for Intelligent Systems, Esslingen University of Applied Sciences, Esslingen, Germany
Uber Advanced Technologies Group（优步先进技术集团）；University of Toronto（多伦多大学）
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling efficient tagging of many attributes and faster learning of new attributes with limited data. Importantly, the embedding is spatio-temporally aware, allowing the model to naturally output spatio-temporal tag values. Values can then be pooled over arbitrary regions, in order to, for example, compute the pedestrian density in front of the SDV, or determine if a car is blocking another car at a 4-way intersection. We demonstr…
Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）；Hangzhou International Innovation Institute, Beihang University（杭州国际创新院，北京航空航天大学）；Meituan, Beijing, China（美团，北京，中国）
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）
EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving
Computer Science, Khalifa University, Abu Dhabi, UAE（计算机科学，卡利法大学，阿布扎赫德，阿联酋）；Computer Science, New York University, Abu Dhabi, UAE（计算机科学，纽约大学，阿布扎赫德，阿联酋）；Computer and Information Engineering, Khalifa University, Abu Dhabi, UAE（计算机与信息工程，卡利法大学，阿布扎赫德，阿联酋）；KUCARS-KU Center for Autonomous Robotic Systems, Khalifa University, Abu Dhabi, UAE（KUCARS-KU自主机器人系统中心，卡利法大学，阿布扎赫德，阿联酋）
With the rise of self-driving vehicles comes the risk of accidents and the need for higher safety, and protection for pedestrian detection in the following scenarios: imminent crashes, thus the car should crash into an object and avoid the pedestrian, and in the case of road intersections, where it is important for the car to stop when pedestrians are crossing. Currently, a special topology of deep neural networks called Fused Deep Neural Network (F-DNN) is considered to be the state of the art in pedestrian detection, as it has the lowest miss rate, yet it is very slow. Therefore, acceleratio…
