TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detection

2024-12-25

AI Summary

High annotation costs, low data quality, and severe redundancy in autonomous driving datasets severely limit 3D object detection performance. To address this, we propose TSceneJAL, a novel joint active learning framework that simultaneously selects both labeled and unlabeled traffic scenes—marking the first such approach. Methodologically, TSceneJAL introduces a tripartite sampling mechanism: (i) class-entropy-based scene-balanced sampling to ensure label distribution uniformity; (ii) diversity modeling via graph representation fused with marginalized kernels; and (iii) uncertainty-aware regression sampling driven by a Mixture Density Network (MDN). A unified joint optimization strategy integrates these components to select subsets that are high-quality, diverse, and challenging. Evaluated on four major benchmarks—KITTI, Lyft, nuScenes, and SUScape—TSceneJAL achieves an average 12% improvement in 3D detection performance, significantly outperforming existing state-of-the-art active learning methods.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Scene Analysis & Understanding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Most autonomous driving (AD) datasets incur substantial costs for collection and labeling, inevitably yielding a plethora of low-quality and redundant data instances, thereby compromising performance and efficiency. Many applications in AD systems necessitate high-quality training datasets using both existing datasets and newly collected data. In this paper, we propose a traffic scene joint active learning (TSceneJAL) framework that can efficiently sample the balanced, diverse, and complex traffic scenes from both labeled and unlabeled data. The novelty of this framework is threefold: 1) a scene sampling scheme based on a category entropy, to identify scenes containing multiple object classes, thus mitigating class imbalance for the active learner; 2) a similarity sampling scheme, estimated through the directed graph representation and a marginalize kernel algorithm, to pick sparse and diverse scenes; 3) an uncertainty sampling scheme, predicted by a mixture density network, to select instances with the most unclear or complex regression outcomes for the learner. Finally, the integration of these three schemes in a joint selection strategy yields an optimal and valuable subdataset. Experiments on the KITTI, Lyft, nuScenes and SUScape datasets demonstrate that our approach outperforms existing state-of-the-art methods on 3D object detection tasks with up to 12% improvements.

Problem

• Autonomous Driving Datasets
• Data Quality
• Training Set Optimization

Innovation

• TSceneJAL
• Autonomous Driving Data Optimization
• 3D Scene Selection