梳理20个公开车道检测数据集，助你选对数据提升自动驾驶性能。
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review
- 按传感器分辨率、标注类型等维度构建多维评估体系
- 分析20个数据集的优缺点，揭示当前数据多样性不足问题
- 适合自动驾驶算法研究者快速定位合适数据集
准确的车道检测对自动驾驶至关重要，可保障车辆在各类道路场景下的安全导航。为支持车道检测算法的开发与评估，已推出众多公开数据集，其差异体现在数据量、传感器类型、标注粒度、环境条件和场景多样性等方面。本文系统综述了20个公开可用的车道检测数据集，从传感器分辨率、标注类型及道路与天气条件多样性等关键指标出发，采用新型多维质量评估方法进行分类分析。通过识别现有挑战与研究空白，指出了未来数据集改进方向，有助于推动鲁棒车道检测技术的创新。本综述可为研究人员选择合适数据集提供参考，助力自动驾驶整体发展。
原文摘要 · Abstract (English)
Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation across a variety of road scenarios. Numerous datasets have been introduced to support the development and evaluation of lane detection algorithms, each differing in terms of the amount of data, sensor types, annotation granularity, environmental conditions, and scenario diversity. This paper provides a comprehensive review of 20 publicly available lane detection datasets, systematically analyzing their characteristics, advantages, and limitations. We classify these datasets based on key performance indicators such as sensor resolution, annotation types and diversity of road and weather conditions using a novel multidimensional metric for dataset quality. By identifying existing challenges and research gaps, we highlight opportunities for future dataset improvements that can further drive innovation in robust lane detection. This review serves as a resource for researchers seeking appropriate datasets for robust lane detection and contributes to the broader goal of advancing autonomous driving.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。