扩充数据集并构建评估框架，提升无人降落系统可靠性
LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems
- 引入必应地图和飞行模拟器数据增强多样性
- 覆盖多跑道机场，优化真实场景落地能力
- 开源模型与评估框架，助力自动驾驶研发
本文针对自主降落系统开发中的数据集局限性问题，重点解决机器学习模型用于目标检测的监督训练难题。主要贡献包括：(1) 增强数据集多样性，倡导引入必应地图航拍图像和飞行模拟器数据，扩展现有数据生成器LARD的生成范围；(2) 优化运行设计域（ODD），解决不现实着陆场景问题，并将覆盖范围扩展至多跑道机场；(3) 构建自主降落系统中目标检测子任务的基准评估框架，支持复杂多实例场景下的模型评测，并提供开源基线模型以衡量AI性能。
原文摘要 · Abstract (English)
This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.
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