Lane detection is a crucial perception task for all levels of automated vehicles (AVs) and Advanced Driver Assistance Systems, particularly in mixed-traffic environments where AVs must interact with human-driven vehicles (HDVs) and challenging traffic scenarios. Current methods lack versatility in delivering accurate, robust, and real-time compatible lane detection, especially vision-based methods often neglect critical regions of the image and their spatial-temporal (ST) salience, leading to poor performance in difficult circumstances such as serious occlusion and dazzle lighting. This study introduces a novel sequential neural network model with a spatial-temporal attention mechanism to focus on key features of lane lines and exploit salient ST correlations among continuous image frames. The proposed model, built on a standard encoder-decoder structure and common neural network backbones, is trained and evaluated on three large-scale open-source datasets. Extensive experiments demonstrate the strength and robustness of the proposed model, outperforming state-of-the-art methods in various testing scenarios. Furthermore, with the ST attention mechanism, the developed sequential neural network models exhibit fewer parameters and reduced Multiply-Accumulate Operations (MACs) compared to baseline sequential models, highlighting their computational efficiency. Relevant data, code, and models are released at https://doi.org/10.4121/4619cab6-ae4a-40d5-af77-582a77f3d821.

翻译：车道线检测是所有级别自动驾驶车辆（AVs）与高级驾驶辅助系统（ADAS）的关键感知任务，尤其在混合交通环境中——自动驾驶车辆需与人类驾驶车辆（HDVs）交互并应对复杂交通场景。现有方法在实现精确、鲁棒且实时兼容的车道线检测方面普遍缺乏通用性，特别是基于视觉的方法常忽略图像关键区域及其时空显著性，导致在严重遮挡与强光干扰等困难场景下性能不佳。本研究提出一种新型的序列神经网络模型，其配备时空注意力机制，能够聚焦于车道线的关键特征并利用连续图像帧间显著的时空相关性。该模型基于标准编码器-解码器架构与通用神经网络骨干构建，在三个大规模开源数据集上进行了训练与评估。大量实验证明了所提模型的优势与鲁棒性，其在多种测试场景中均优于现有先进方法。此外，得益于时空注意力机制，所开发的序列神经网络模型相较于基线序列模型具有更少的参数量与乘积累加运算量（MACs），突显了其计算高效性。相关数据、代码与模型已发布于 https://doi.org/10.4121/4619cab6-ae4a-40d5-af77-582a77f3d821。