Collective Perception Datasets for Autonomous Driving: A Comprehensive Review
为了确保自动驾驶汽车在复杂的城市环境中安全运行，需要全面感知环境。然而，由于环境条件、传感器限制和遮挡等因素，从单一视角获得完整的感知是不可能的。为解决这个问题，众包感知是一种有效的技术。训练和评估众包感知方法需要真实的大型数据集。本文是对自动驾驶背景下众包感知数据集的首次全面技术审查。调查分析了现有的V2V和V2X数据集，根据不同的标准将它们分类。重点关注它们在开发连接式自动驾驶汽车中的应用。本研究旨在识别所有数据集的关键标准，并呈现它们的优缺点和异常。最后，调查建议关于哪个数据集最适合用于众包3D物体检测、跟踪和语义分割。
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.
https://arxiv.org/abs/2405.16973
https://arxiv.org/pdf/2405.16973.pdf