Mixed Signals V2X Dataset

Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X dataset featuring 45.1k point clouds and 240.6k bounding boxes collected from three connected autonomous vehicles (CAVs) equipped with two different types of LiDAR sensors, plus a roadside unit with dual LiDARs. Our dataset provides precisely aligned point clouds and bounding box annotations across 10 classes, ensuring reliable data for perception training. We provide detailed statistical analysis on the quality of our dataset and extensively benchmark existing V2X methods on it. Mixed Signals V2X Dataset is one of the highest quality, large-scale datasets publicly available for V2X perception research: https://mixedsignalsdataset.cs.cornell.edu/.

车与万物互联（Vehicle-to-Everything, V2X）协同感知已成为解决单车感知系统局限的极具前景的技术方案。然而现有V2X数据集在覆盖范围、数据多样性与质量层面均存在明显不足。为填补这一研究空白，本工作推出名为《Mixed Signals》的综合V2X数据集：该数据集包含45.1千个点云与240.6千个边界框，采集自三台搭载两种不同型号激光雷达（LiDAR）的联网自动驾驶车辆（Connected Autonomous Vehicles, CAVs），以及一台搭载双激光雷达的路边单元。本数据集提供了跨10个类别的精准配准点云与边界框标注，可为感知模型训练提供可靠的数据支撑。我们针对本数据集的质量开展了详尽的统计分析，并在该数据集上对现有V2X感知方法进行了全面的基准测试。《Mixed Signals V2X数据集》是目前公开可用的、用于V2X感知研究的高质量大规模数据集之一，其官方访问链接为：https://mixedsignalsdataset.cs.cornell.edu。

搜集汇总

背景与挑战

背景概述

Mixed Signals V2X Dataset是一个高质量、大规模的车联网协同感知数据集，旨在解决现有数据集在范围、多样性和质量上的不足。它包含从三辆联网自动驾驶车辆和一个路侧单元采集的45.1k点云和240.6k边界框，覆盖10个类别，并提供了精确对齐的标注，适用于感知训练和研究。