analiza aiqbench
This research introduces a modular pipeline for generating terabyte-scale, ground-truth-labeled datasets tailored for multi-agent and multi-domain autonomous systems. By leveraging the AVstack framework and CARLA simulator, the approach addresses critical gaps in existing data regarding diversity and coordination across ground, aerial, and infrastructure contexts. The resulting synthetic data enables controlled experimentation on perception and fusion tasks, offering a scalable solution for training collaborative autonomy in complex environments.
Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems
arXiv:2606.04444v1 Announce Type: cross Abstract: Existing datasets cannot support large-scale learning in multi-agent, multi-sensor, or multi-domain autonomy, where diversity and coordination are essential. We present a modular dataset generation pipeline that creates terabyte-scale, ground-truth-labeled data for ground, aerial, and infrastructure-based systems using the AVstack framework and CARLA simulator. Supporting single- and multi-agent configurations with flexible sensor suites, the pipeline enables controllable experimentation across challenging conditions. Representative perception and fusion studies show how generated data can support application-specific training and collaborative autonomy.
Abstract:Existing datasets cannot support large-scale learning in multi-agent, multi-sensor, or multi-domain autonomy, where diversity and coordination are essential. We present a modular dataset generation pipeline that creates terabyte-scale, ground-truth-labeled data for ground, aerial, and infrastructure-based systems using the AVstack framework and CARLA simulator. Supporting single- and multi-agent configurations with flexible sensor suites, the pipeline enables controllable experimentation across challenging conditions. Representative perception and fusion studies show how generated data can support application-specific training and collaborative autonomy.
Image and Video Processing (eess.IV); Machine Learning (cs.LG)
arXiv:2606.04444 [eess.IV]
(or arXiv:2606.04444v1 [eess.IV] for this version)
https://doi.org/10.48550/arXiv.2606.04444 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 175 cuvinte