Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational inefficiencies and feature competition between tasks when using traditional multi-task learning approaches. This paper addresses these issues by proposing a novel unified representation, RepVF, which harmonizes the representation of various perception tasks such as 3D object detection and 3D lane detection within a single framework. RepVF characterizes the structure of different targets in the scene through a vector field, enabling a single-head, multi-task learning model that significantly reduces computational redundancy and feature competition. Building upon RepVF, we introduce RFTR, a network designed to exploit the inherent connections between different tasks by utilizing a hierarchical structure of queries that implicitly model the relationships both between and within tasks. This approach eliminates the need for task-specific heads and parameters, fundamentally reducing the conflicts inherent in traditional multi-task learning paradigms. We validate our approach by combining labels from the OpenLane dataset with the Waymo Open dataset. Our work presents a significant advancement in the efficiency and effectiveness of multi-task perception in autonomous driving, offering a new perspective on handling multiple 3D perception tasks synchronously and in parallel. The code will be available at: https://github.com/jbji/RepVF
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）
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）
Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors
Centre for Secure Information Technologies (CSIT), Queen's University Belfast, UK（安全信息科技中心（CSIT），女王大学贝尔法斯特，英国）；Department of Information Engineering and Computer Science, University of Trento, Italy（信息工程与计算机科学系，特伦托大学，意大利）
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）
Bandwidth-adaptive Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehicles
Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, L-1855, Luxembourg（安全、可靠性与信任跨学科研究中心（SnT），卢森堡大学）；Computer Science Department, Vassar College, Poughkeepsie, NY 12604, USA（计算机科学系，瓦萨学院）
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与出行学院）；DiDi Global（滴滴全球公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
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）
Scene-Aware Vectorized Memory Multi-Agent Framework with Cross-Modal Differentiated Quantization VLMs for Visually Impaired Assistance
School of Information；Software Engineering, University of Electronic Science；Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences \& Sichuan Provincial People's Hospital, University of Electronic Science；Faculty of Computing, Harbin Institute of Technology, Harbin, China
Hybrid Perception and Equivariant Diffusion for Robust Multi-Node Rebar Tying
Tsinghua University, Shenzhen International Graduate School, Shenzhen, China（清华大学深圳国际研究生院，深圳，中国）；Department of Mechanical Engineering, University of California, Berkeley（加州大学伯克利分校机械工程系）；Hong Kong Center for Construction Robotics, The Hong Kong University of Science and Technology（香港科技大学建设机器人中心）
UniVector: Unified Vector Extraction via Instance-Geometry Interaction
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；School of Automation, Central South University（自动化学院，中南大学）；Department of Geomatics Engineering, Changsha University of Science and Technology（测绘工程系，长沙理工大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Machine Learning Group（德累斯顿-罗斯托克研究所（HZDR），资源技术研究所，机器学习组）；Lancaster University（兰卡斯特大学）
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
Université de Montréal（蒙特利尔大学）；JHU APL（约翰斯·霍普金斯大学应用物理实验室）；JHU（约翰斯·霍普金斯大学）；UMass（马萨诸塞大学）；DEVCOM ARL（美国陆军作战能力发展司令部陆军研究实验室）；MIT（麻省理工学院）；University of Toronto（多伦多大学）
