A Feedback-Control Framework for Efficient Dataset Collection from In-Vehicle Data Streams
This addresses the problem of inefficient and costly dataset collection for data-centric AI systems, representing a novel paradigm shift rather than an incremental improvement.
The paper tackles the inefficiency of open-loop data collection in AI by introducing Feedback Control Data Collection (FCDC), a closed-loop framework that uses online probabilistic models and feedback signals to regulate sample retention, resulting in 25.9% more balanced datasets and 39.8% reduced storage on real data streams.
Modern AI systems are increasingly constrained not by model capacity but by the quality and diversity of their data. Despite growing emphasis on data-centric AI, most datasets are still gathered in an open-loop manner which accumulates redundant samples without feedback from the current coverage. This results in inefficient storage, costly labeling, and limited generalization. To address this, this paper introduces Feedback Control Data Collection (FCDC), a paradigm that formulates data collection as a closed-loop control problem. FCDC continuously approximates the state of the collected data distribution using an online probabilistic model and adaptively regulates sample retention using based on feedback signals such as likelihood and Mahalanobis distance. Through this feedback mechanism, the system dynamically balances exploration and exploitation, maintains dataset diversity, and prevents redundancy from accumulating over time. In addition to demonstrating the controllability of FCDC on a synthetic dataset that converges toward a uniform distribution under Gaussian input assumption, experiments on real data streams show that FCDC produces more balanced datasets by 25.9% while reducing data storage by 39.8%. These results demonstrate that data collection itself can be actively controlled, transforming collection from a passive pipeline stage into a self-regulating, feedback-driven process at the core of data-centric AI.