Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
This addresses the problem of limited datasets for V2X perception research, offering a comprehensive resource for researchers in autonomous driving, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of diverse and high-quality datasets for vehicle-to-everything (V2X) collaborative perception by introducing Mixed Signals, a dataset with 45.1k point clouds and 240.6k bounding boxes from multiple LiDAR configurations, which they benchmarked to provide reliable data for perception training.
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 configurations of LiDAR sensors, plus a roadside unit with dual LiDARs. Our dataset provides 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. The Mixed Signals dataset is ready-to-use, with precise alignment and consistent annotations across time and viewpoints. Dataset website is available at https://mixedsignalsdataset.cs.cornell.edu/.