Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving assistant systems (ADAS). Despite advances from image processing to deep learning based models, algorithm performance is highly dependent on training data matching the local challenges such as extreme lighting conditions, partially visible lane markings, and sparse lane markings like Botts' dots. To address this, we present an end-to-end lane detection and classification system based on deep learning methodologies. In our study, we introduce a unique dataset meticulously curated to encompass scenarios that pose significant challenges for state-of-the-art (SOTA) lane localization models. Moreover, we propose a CNN-based classification branch, seamlessly integrated with the detector, facilitating the identification of distinct lane types. This architecture enables informed lane-changing decisions and empowers more resilient ADAS capabilities. We also investigate the effect of using mixed precision training and testing on different models and batch sizes. Experimental evaluations conducted on the widely-used TuSimple dataset, Caltech Lane dataset, and our LVLane dataset demonstrate the effectiveness of our model in accurately detecting and classifying lanes amidst challenging scenarios. Our method achieves state-of-the-art classification results on the TuSimple dataset. The code of the work can be found on www.github.com/zillur-av/LVLane.

翻译：车道检测在自动驾驶车辆与高级驾驶辅助系统（ADAS）领域发挥着关键作用。尽管技术已从图像处理发展到基于深度学习的方法，但算法性能高度依赖于匹配本地化挑战的训练数据，例如极端光照条件、部分可见的车道标记、以及Botts' dots等稀疏车道标识。为解决此问题，我们提出了一种基于深度学习的端到端车道检测与分类系统。本研究引入了一套独特的数据集，该数据集经过精心策划，覆盖了对现有最先进（SOTA）车道定位模型构成重大挑战的场景。此外，我们提出了一种与检测器无缝集成的CNN分类分支，能够支持不同车道类型的辨识。该架构使车道变更决策具备信息感知能力，并增强ADAS功能的鲁棒性。我们还研究了在不同模型和批次大小下使用混合精度训练与测试的效果。在广泛使用的TuSimple数据集、Caltech Lane数据集以及我们构建的LVLane数据集上进行的实验评估表明，本模型能在挑战性场景中精准完成车道检测与分类任务。所提方法在TuSimple数据集上取得了最先进的分类结果。相关代码已发布于www.github.com/zillur-av/LVLane。