Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated. This study proposes a novel hybrid spatial–temporal (ST) sequence-to-one deep learning architecture. This architecture makes full use of the ST information in multiple continuous image frames to detect the lane markings in the very last frame. Specifically, the hybrid model integrates the following aspects: (a) the single image feature extraction module equipped with the spatial convolutional neural network; (b) the ST feature integration module constructed by ST recurrent neural network; (c) the encoder–decoder structure, which makes this image segmentation problem work in an end-to-end supervised learning format. Extensive experiments reveal that the proposed model architecture can effectively handle challenging driving scenes and outperforms available state-of-the-art methods.

中文翻译：

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一种用于车道检测的混合时空深度学习架构

准确可靠的车道检测对于车道保持辅助和车道偏离警告系统的安全性能至关重要。然而，在某些具有挑战性的情况下，很难像当前文献中所做的那样从一张图像中准确检测车道，从而获得令人满意的性能。由于车道标记是连续的线，因此如果结合先前帧的信息，则可以更好地推断出当前单幅图像中难以准确检测到的车道。本研究提出了一种新颖的混合时空 (ST) 序列对一深度学习架构。该体系结构充分利用多个连续图像帧中的 ST 信息来检测最后一帧中的车道标记。具体来说，混合模型集成了以下几个方面：(a) 搭载空间卷积神经网络的单幅图像特征提取模块；(b) ST递归神经网络构建的ST特征集成模块；(c) 编码器-解码器结构，这使得这个图像分割问题以端到端的监督学习格式工作。大量实验表明，所提出的模型架构可以有效地处理具有挑战性的驾驶场景，并且优于可用的最先进方法。