利用路侧标识柱检测积雪乡村道路车道，不依赖被遮挡的标线。
SnowyLane: Robust Lane Detection on Snow-covered Rural Roads Using Infrastructural Elements
- 通过识别路侧竖直标识柱间接推断车道位置。
- 在重度积雪覆盖下，检测准确率显著优于现有方法。
- 适合冬季复杂路况下的自动驾驶系统研发者使用。
积雪环境中的自动驾驶车道检测仍面临重大挑战，主要因车道标线常因积雪覆盖或遮挡而失效。本文提出一种新型、鲁棒且实时可行的方法，摒弃对传统车道标线的依赖，转而通过检测路侧特征——即称为delineators的垂直路侧柱作为间接车道指示。该方法首先感知这些标识柱，再利用参数化贝塞尔曲线模型拟合平滑车道轨迹，结合空间一致性与道路几何约束。为支持此类场景的训练与评估，我们构建了SnowyLane数据集，包含8万张合成标注图像，涵盖不同积雪程度与光照条件的冬季驾驶场景。相比现有先进车道检测系统，本方法在恶劣天气下表现出显著更强的鲁棒性，尤其在重雪遮挡情况下。本工作为冬季自动驾驶的可靠车道检测奠定了基础，并提供了宝贵的多气候研究资源。数据集已公开于https://ekut-es.github.io/snowy-lane。
原文摘要 · Abstract (English)
Lane detection for autonomous driving in snow-covered environments remains a major challenge due to the frequent absence or occlusion of lane markings. In this paper, we present a novel, robust and realtime capable approach that bypasses the reliance on traditional lane markings by detecting roadside features,specifically vertical roadside posts called delineators, as indirect lane indicators. Our method first perceives these posts, then fits a smooth lane trajectory using a parameterized Bezier curve model, leveraging spatial consistency and road geometry. To support training and evaluation in these challenging scenarios, we introduce SnowyLane, a new synthetic dataset containing 80,000 annotated frames capture winter driving conditions, with varying snow coverage, and lighting conditions. Compared to state-of-the-art lane detection systems, our approach demonstrates significantly improved robustness in adverse weather, particularly in cases with heavy snow occlusion. This work establishes a strong foundation for reliable lane detection in winter scenarios and contributes a valuable resource for future research in all-weather autonomous driving. The dataset is available at https://ekut-es.github.io/snowy-lane
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