Shaif Chowdhury, Akhila Yeruvasu, Moshiur Rahman Tonmoy, Ayush Goyal, Dunren Che · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28508 · open-access PDF
可靠的轨道标记对于人类驾驶员和自动驾驶系统都很重要,但它们的状态仍然通过手动检查被普遍评估。现有的自动驾驶数据集主要支持轨道检测和场景理解,通常缺乏以维护为导向的标签,用于轨道标记的可见性、连续性或降解。在本研究中,我们介绍了一个地理参考的智能手机 dashcam 数据集和基线框架,用于轨道标记质量评估。
Machine translation (Meta M2M100); the English original follows.
Reliable lane markings are important for both human drivers and automated driving systems, but their condition is still commonly assessed through manual inspection. Existing autonomous-driving datasets primarily support lane detection and scene understanding and usually lack maintenance-oriented labels for lane-marking visibility, continuity, or degradation. In this study, we present a geo-referenced smartphone dashcam dataset and baseline framework for lane-marking quality assessment. The dataset contains 63,839 road-facing frames collected across South Texas using a GPS-enabled dashcam appli
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