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
The increasing complexity of autonomous systems requires scalable and diverse datasets that current methods cannot provide, pushing researchers to develop new generation pipelines.
This development addresses a critical bottleneck in the advancement of multi-agent and multi-sensor autonomy, paving the way for more robust and reliable AI systems across various domains.
The ability to generate terabyte-scale, ground-truth-labeled data for diverse autonomous configurations will accelerate research and development in AI for complex robotic and vehicular systems.
- · Autonomous vehicle developers
- · AI research institutions
- · Defense contractors
- · Robotics companies
- · Companies reliant on bespoke, small-scale datasets
- · AI development lagging in data generation capabilities
The availability of large, diverse datasets will significantly improve the training and evaluation of autonomous systems.
Faster iterative development cycles for AI models will lead to more advanced and widespread deployment of autonomous technology.
The enhanced performance and reliability of autonomous systems could accelerate the transition to fully agentic applications in real-world environments.
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Read at arXiv cs.LG