# Active Data Acquisition in Autonomous Driving Simulation

arXiv: https://arxiv.org/abs/2306.13923 · HTML: https://mlsanity.com/paper/2306.13923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm](https://mlsanity.com/paper/2005.04166/) (2020)
- [Learning Directed Locomotion in Modular Robots with Evolvable Morphologies](https://mlsanity.com/paper/2001.07804/) (2020)
- [A Semantic Web Technology Index](https://mlsanity.com/paper/2201.07034/) (2022)
- [Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3](https://mlsanity.com/paper/1812.10968/) (2018)

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

- [Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models](https://mlsanity.com/paper/2605.01662/) (2026)

## Abstract (arXiv)

Autonomous driving algorithms rely heavily on learning-based models, which require large datasets for training. However, there is often a large amount of redundant information in these datasets, while collecting and processing these datasets can be time-consuming and expensive. To address this issue, this paper proposes the concept of an active data-collecting strategy. For high-quality data, increasing the collection density can improve the overall quality of the dataset, ultimately achieving similar or even better results than the original dataset with lower labeling costs and smaller dataset sizes. In this paper, we design experiments to verify the quality of the collected dataset and to demonstrate this strategy can significantly reduce labeling costs and dataset size while improving the overall quality of the dataset, leading to better performance of autonomous driving systems. The source code implementing the proposed approach is publicly available on https://github.com/Th1nkMore/carla_dataset_tools.