{"ID":3052302,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T04:39:12.706778348Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04444","arxiv_id":"2606.04444","title":"Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.04444","url_pdf":"https://arxiv.org/pdf/2606.04444v1","authors":"[\"R. Spencer Hallyburton\",\"David Hunt\",\"Miroslav Pajic\"]","published":"2026-06-03T04:46:44Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.LG\"]","methods":"[]","has_code":false}