V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University（上海交通大学合作 medianet 创新中心）；Shanghai AI Laboratory（上海人工智能实验室）；Sangfor Technologies Inc.（深信服科技股份有限公司）
The objective of the collaborative vehicle-to-everything perception task is to enhance the individual vehicle's perception capability through message communication among neighboring traffic agents. Previous methods focus on achieving optimal performance within bandwidth limitations and typically adopt BEV maps as the basic collaborative message units. However, we demonstrate that collaboration with dense representations is plagued by object feature destruction during message packing, inefficient message aggregation for long-range collaboration, and implicit structure representation communication. To tackle these issues, we introduce a brand new message unit, namely point cluster, designed to represent the scene sparsely with a combination of low-level structure information and high-level semantic information. The point cluster inherently preserves object information while packing messages, with weak relevance to the collaboration range, and supports explicit structure modeling. Building upon this representation, we propose a novel framework V2X-PC for collaborative perception. This framework includes a Point Cluster Packing (PCP) module to keep object feature and manage bandwidth through the manipulation of cluster point numbers. As for effective message aggregation, we propose a Point Cluster Aggregation (PCA) module to match and merge point clusters associated with the same object. To further handle time latency and pose errors encountered in real-world scenarios, we propose parameter-free solutions that can adapt to different noisy levels without finetuning. Experiments on two widely recognized collaborative perception benchmarks showcase the superior performance of our method compared to the previous state-of-the-art approaches relying on BEV maps.
Vehicle-to-Everything Cooperative Perception for Autonomous Driving
College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；Momoni AI；Department of Electrical and Computer Engineering, University of Alabama in Huntsville（电气与计算机工程系，阿拉巴马大学亨茨维尔分校）；Department of Automation and Intelligent Sensing, Shanghai Jiaotong University（自动化与智能感知系，上海交通大学）；School of Engineering, Swinburne University of Technology（工程学院，斯威本技术大学）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（信息与通信研究机构，科技研究局（A*STAR））
University of California, Los Angeles（加州大学洛杉矶分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google Research（谷歌研究院）；University of California, Merced（加州大学默塞德分校）
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust cooperative perception framework with V2X communication using a novel vision Transformer. Specifically, we build a holistic attention model, namely V2X-ViT, to effectively fuse information across on-road agents (i.e., vehicles and infrastructure). V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention, which captures inter-agent interaction and per-agent spat…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: \textit{how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment?} Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive an…
New York University（纽约大学）；University of Southern California（南加州大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving. However, the lack of a public dataset significantly restricts the research progress of collaborative perception. To fill this gap, we present V2X-Sim, a comprehensive simulated multi-agent perception dataset for V2X-aided autonomous driving. V2X-Sim provides: (1) \hl{multi-agent} sensor recordings from the road-side unit (RSU) and multiple vehicles that enable collab…
Tongji University（同济大学）；Westwell lab（西井实验室）；Technische Universität München（慕尼黑工业大学）
Multi-agent collaborative perception (MCP) has recently attracted much attention. It includes three key processes: communication for sharing, collaboration for integration, and reconstruction for different downstream tasks. Existing methods pursue designing the collaboration process alone, ignoring their intrinsic interactions and resulting in suboptimal performance. In contrast, we aim to propose a Unified Collaborative perception framework named UMC, optimizing the communication, collaboration, and reconstruction processes with the Multi-resolution technique. The communication introduces a n…
Collaborative Perception Datasets for Autonomous Driving: A Review
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing)（中国矿业大学（北京）机械与电子工程学院）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与移动系统国家重点实验室）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Mechatronics Engineering, Harbin Institute of Technology（哈尔滨工业大学机械电子工程学院）；Department of Electronic & Electrical Engineering, University of Bath（巴斯大学电子与电气工程系）
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学合作媒体网络创新中心）；University of Southern California（南加州大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. It empowers agents to only share spatially sparse, yet perceptually critical information, contributing to where to communicate. Based on this novel spatial confidence m…
CERF: Communication-Efficient and Retraining-Free Collaborative Perception
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
CoST: Efficient Collaborative Perception From Unified Spatiotemporal Perspective
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；University of California, Los Angeles（美国加州大学洛杉矶分校）
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception. Existing collaborative perception methods usually consider an ideal communication environment. However, in practice, the communication system inevitably suffers from latency issues, causing potential performance degradation and high risks in safety-critical applications, such as autonomous driving. To mitigate the effect caused by the inevitable latency, from a machine learning perspective, we present the first latency-aware collaborative perception system, which activel…
CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；City University of Hong Kong（香港城市大学）
Perception is one of the crucial module of the autonomous driving system, which has made great progress recently. However, limited ability of individual vehicles results in the bottleneck of improvement of the perception performance. To break through the limits of individual perception, collaborative perception has been proposed which enables vehicles to share information to perceive the environments beyond line-of-sight and field-of-view. In this paper, we provide a review of the related work about the promising collaborative perception technology, including introducing the fundamental concep…
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统上海研究院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Mechatronic Engineering and Automation, Shanghai University（上海大学机械电子工程与自动化学院）
CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion
Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education（交通运输大数据与人工智能重点实验室，教育部）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Xiamen University（厦门大学）
Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective
XITASO GmbH（XITASO公司）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄大学）；Technische Hochschule Ingolstadt (THI)（因戈尔施塔特技术大学）；CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)（CARISSMA电动、连接与安全移动研究所）；Federal University of Santa Catarina (UFSC)（圣卡塔琳娜联邦大学）
School of Information, Renmin University of China（中国人民大学信息学院）；Sony Research and Development Center China（索尼（中国）研发有限公司）；National University of Defense Technology（国防科技大学）
