自动驾驶中的集体感知数据集：综合评述
Collective Perception Datasets for Autonomous Driving: A Comprehensive Review
AI总结：
本文首次全面评述自动驾驶中的集体感知数据集，分析V2V和V2X数据集，并推荐最适合集体3D目标检测、跟踪和语义分割的数据集。
摘要：
为了确保自动驾驶车辆在复杂城市环境中的安全运行，对环境进行完整感知是必要的。然而，由于环境条件、传感器限制和遮挡，从单一视角往往无法实现这一点。为解决这一问题，集体感知是一种有效的方法。真实且大规模的数据集对于训练和评估集体感知方法至关重要。本文首次对自动驾驶背景下的集体感知数据集进行了全面的技术评述。该调查分析了现有的V2V和V2X数据集，并根据传感器模态、环境条件和场景多样性等不同标准对其进行分类。重点在于它们对联网自动驾驶汽车开发的适用性。本研究旨在识别所有数据集的关键标准，并呈现其优点、缺点和异常情况。最后，本调查通过提出关于哪个数据集最适合集体3D目标检测、跟踪和语义分割的建议来结束。
英文摘要：
To ensure safe operation of autonomous vehicles in complex urban environments, complete perception of the environment is necessary. However, due to environmental conditions, sensor limitations, and occlusions, this is not always possible from a single point of view. To address this issue, collective perception is an effective method. Realistic and large-scale datasets are essential for training and evaluating collective perception methods. This paper provides the first comprehensive technical review of collective perception datasets in the context of autonomous driving. The survey analyzes existing V2V and V2X datasets, categorizing them based on different criteria such as sensor modalities, environmental conditions, and scenario variety. The focus is on their applicability for the development of connected automated vehicles. This study aims to identify the key criteria of all datasets and to present their strengths, weaknesses, and anomalies. Finally, this survey concludes by making recommendations regarding which dataset is most suitable for collective 3D object detection, tracking, and semantic segmentation.
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。
