arXiv:2605.21372v1 Announce Type: cross Abstract: Data scaling is fundamental to modern deep learning, and grows increasingly critical as autonomous driving shifts to end-to-end learning. Real-world driving data is expensive to annotate and scene-biased, making real-synthetic co-training with near-infinite synthetic data a promising direction. However, naively incorporating all available synthetic data is inefficient and leads to distribution shifts, and optimizing data mixture under practical training budgets remains a critical yet under-explored problem. In this sense, we claim that the mixt
The increasing reliance on end-to-end learning for autonomous driving makes data scaling a critical bottleneck, pressing the need for optimized real-synthetic co-training methodologies.
Improving the efficiency of incorporating synthetic data will accelerate the development and deployment of autonomous driving systems, reducing costs and overcoming real-world data limitations.
This research outlines a method to better integrate synthetic data, suggesting a more efficient and effective path for training advanced AI models for perception and control in robotics.
- · Autonomous vehicle developers
- · Robotics companies
- · AI data generation platforms
- · Logistics and transportation sectors
- · Companies reliant solely on real-world data collection
- · Hardware-centric AV component suppliers
More robust and rapidly developed autonomous driving systems emerge due to optimized data strategies.
Reduced investment in physically collecting and annotating real-world driving data, shifting resources to synthetic data generation and optimization.
Broader applications of effective real-synthetic co-training across other robotics and AI domains, beyond just autonomous vehicles.
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Read at arXiv cs.LG