Collaborative Perception Datasets in Autonomous Driving: A Survey
Melih Yazgan, Mythra Varun Akkanapragada, J. Marius Zoellner
TL;DR
The paper addresses accelerating autonomous-driving perception through collaborative sensing by surveying large-scale, multi-modal datasets across V2V, V2I, and V2X. It catalogs and compares road-intersection and collaborative-perception datasets, analyzing sensor configurations, data modalities, licenses, and downstream task applicability. Key findings include a dominance of simulated data, domain-shift and realism gaps, sensor heterogeneity challenges, and limited real-world data availability, alongside privacy and security considerations in data sharing. The authors advocate for globally accessible, diverse benchmarks and standardized evaluation to advance cooperative perception systems and their deployment.
Abstract
This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X). It highlights the latest developments in large-scale benchmarks that accelerate advancements in perception tasks for autonomous vehicles. The paper systematically analyzes a variety of datasets, comparing them based on aspects such as diversity, sensor setup, quality, public availability, and their applicability to downstream tasks. It also highlights the key challenges such as domain shift, sensor setup limitations, and gaps in dataset diversity and availability. The importance of addressing privacy and security concerns in the development of datasets is emphasized, regarding data sharing and dataset creation. The conclusion underscores the necessity for comprehensive, globally accessible datasets and collaborative efforts from both technological and research communities to overcome these challenges and fully harness the potential of autonomous driving.