Collaborative Perception Datasets in Autonomous Driving: A Survey

TL;DR

This survey reviews collaborative perception datasets for autonomous driving, analyzing their features, challenges, and the need for comprehensive, accessible datasets to advance perception tasks in V2X systems.

Contribution

It systematically compares existing datasets, discusses key challenges, and emphasizes the importance of collaborative efforts to develop better, more diverse autonomous driving datasets.

Findings

Highlights the diversity and limitations of current datasets.

Identifies key challenges like domain shift and privacy concerns.

Calls for globally accessible, comprehensive datasets.

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.