CoopScenes：推进自动驾驶中协同感知的多场景基础设施与车辆数据
CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving
AI总结：
本文提出大规模多场景车路协同数据集 CoopScenes，提供高精度时空同步、点云配准、自动标注与匿名化流程，以支持自动驾驶协同感知研究。
摘要：
城市环境日益复杂，凸显了有效协同感知系统的潜力。为应对这些挑战，我们提出 CoopScenes 数据集，这是一个大规模、多场景数据集，提供来自自车和配套基础设施的同步传感器数据。该数据集以10 Hz提供104分钟的空间与时间同步数据，共产生62,000帧。其实现了具有竞争力的同步性能，平均偏差仅为2.3 ms。此外，数据集包含一种用于精确配准自车与基础设施传感器点云数据的新流程、自动化标注流水线，以及面向人脸和车牌的开源匿名化流水线。数据集覆盖9个不同场景中的100项机动操作，场景包括德国斯图加特地区三座城市的公共交通枢纽、城市施工现场和高速乡村道路。完整数据集共计527 GB，以 .4mse 格式提供，可通过我们完善的开发工具包便捷访问。通过提供精确的大规模数据，CoopScenes 推动协同感知、实时传感器配准和面向城市出行的协作智能系统研究，包括基于机器学习的方法。
英文摘要：
The increasing complexity of urban environments has underscored the potential of effective collective perception systems. To address these challenges, we present the CoopScenes dataset, a large-scale, multi-scene dataset that provides synchronized sensor data from both the ego-vehicle and the supporting infrastructure.The dataset provides 104 minutes of spatially and temporally synchronized data at 10 Hz, resulting in 62,000 frames. It achieves competitive synchronization with a mean deviation of only 2.3 ms. Additionally the dataset includes a novel procedure for precise registration of point cloud data from the ego-vehicle and infrastructure sensors, automated annotation pipelines, and an open-source anonymization pipeline for faces and license plates. Covering nine diverse scenes with 100 maneuvers, the dataset features scenarios such as public transport hubs, city construction sites, and high-speed rural roads across three cities in the Stuttgart region, Germany. The full dataset amounts to 527 GB of data and is provided in the .4mse format, making it easily accessible through our comprehensive development kit. By providing precise, large-scale data, CoopScenes facilitates research in collective perception, real-time sensor registration, and cooperative intelligent systems for urban mobility, including machine learning-based approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
