发表机构
Institute of Measurement and Control Systems, Karlsruhe Institute of Technology; Institute of Automotive Technology, Technical University of Munich; Professorship of Autonomous Vehicle Systems, Technical University of Munich; University of Modena and Reggio Emilia; Humda Lab, Széchenyi István University; Politecnico di Milano; Constructor University; Beijing Institute of Technology; Nanyang Technological University; Code 19 Racing; Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（测量与控制系统研究所，卡尔斯鲁厄理工学院; 汽车技术研究所，慕尼黑工业大学; 自动驾驶车辆系统教授职位，慕尼黑工业大学; 摩德纳大学和雷焦艾米利亚大学; 胡姆达实验室，塞切尼·伊什特万大学; 米兰理工大学; 康斯坦丁大学; 北京理工大学; 南洋理工大学; 代码19赛车; 慕尼黑机器人与机器智能研究所（MIRMI），慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍A2RL V下标max开源数据集，专为高速自动驾驶和多车辆交互感知任务设计，含多样场景数据及专业标注激光雷达点云，以开发者友好格式提供，还对相关检测和跟踪方法做了实现与评估。
AI 中文摘要
在自动驾驶发展中，感知数据集至关重要，它为自动驾驶车辆多模态感知系统的算法训练、测试和验证提供基础数据。目前多数研究集中于结构化城市环境数据集。本文介绍A2RL V下标max开源数据集，专为高速自动驾驶和多车辆交互的感知任务设计。该数据集于2024年阿布扎比自动驾驶赛车联赛期间在亚斯码头F1赛道采集，包含不同场景数据，有近30000个专业标注的激光雷达点云及雷达点云，是自动驾驶赛车领域首个含此类标注的大规模数据集，数据格式便于开发者使用，还对现成3D检测和跟踪方法进行了实现与评估。
英文摘要
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's multimodal perception systems. So far, most research has concentrated on providing datasets for well-structured urban environments. This work introduces the A2RL V\textsubscript{max} open-source dataset, specifically designed for perception tasks in high-speed autonomous driving and multi-vehicle interaction. The dataset was captured during the 2024 Abu Dhabi Autonomous Racing League (A2RL), held at the Yas Marina F1 Circuit, with participation from all competing teams. It contains diverse scenarios, including single-vehicle data at varying speeds, multi-vehicle sessions, and the full final four-vehicle race. The dataset contains almost 30,000 professionally annotated LiDAR point clouds, along with RADAR point clouds. In particular, it is the first large-scale dataset in autonomous racing to feature professionally annotated LiDAR point clouds, enabling deep learning-based perception research. The data is provided in a developer-friendly format, enabling easy implementation and evaluation in future research. We provide implementation and evaluation for off-the-shelf 3D detection and tracking methods. Although baseline methods show promising results for both 3D detection and tracking, specialized methods are required to address the unique challenges of high-speed autonomous driving. For a detailed description of the dataset, please visit the \href{https://tum-avs.github.io/A2RL_Dataset_website/}{A2RL V\textsubscript{max} Dataset Website}
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