# A Feedback-Control Framework for Efficient Dataset Collection from In-Vehicle Data Streams

arXiv: https://arxiv.org/abs/2511.03239 · HTML: https://mlsanity.com/paper/2511.03239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation](https://mlsanity.com/paper/2104.14812/) (2021)
- [DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning](https://mlsanity.com/paper/2204.08499/) (2022)
- [The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation](https://mlsanity.com/paper/1904.03215/) (2019)
- ["Curse of rarity" for autonomous vehicles](https://mlsanity.com/paper/2207.02749/) (2022)
- [An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving](https://mlsanity.com/paper/2103.03678/) (2021)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [A Novel Multi-layer Framework for Tiny Obstacle Discovery](https://mlsanity.com/paper/1904.10161/) (2019)
- [EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application](https://mlsanity.com/paper/2309.15492/) (2023)
- [CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research](https://mlsanity.com/paper/2404.17550/) (2024)
- [Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets](https://mlsanity.com/paper/2312.01860/) (2023)
- [A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording](https://mlsanity.com/paper/2507.04529/) (2025)
- [Behavior Forests: Real-Time Discovery of Dynamic Behavior for Data Selection](https://mlsanity.com/paper/2407.02008/) (2024)

## Built on by (most cited first, 1 of 1)

- [From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection](https://mlsanity.com/paper/2603.29474/) (2026)

## Abstract (arXiv)

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.