Abstract
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 this https URL.
Abstract (translated)
车道检测是所有级别自动驾驶汽车（AV）和高级驾驶员辅助系统的一项关键感知任务，尤其是在混合交通环境中，自动驾驶汽车必须与人力驾驶的车辆（HDV）互动，并处理复杂的交通场景。目前的方法在提供准确、稳健且实时兼容的车道检测方面缺乏灵活性，特别是基于视觉的方法常常忽视图像的关键区域及其空间-时间（ST）显著性，导致在严重遮挡和眩光照明等困难情况下表现不佳。 本研究提出了一种具有空间-时间注意力机制的新颖序列神经网络模型，专注于车道线的关键特征，并利用连续图像帧中的显著ST相关性。所提出的模型基于标准的编码器-解码器结构和通用的神经网络骨干，在三个大规模开源数据集上进行了训练和评估。广泛的实验展示了该模型的强大性和鲁棒性，在各种测试场景中超越了现有的最先进方法。此外，凭借ST注意力机制，开发出的序列神经网络模型相比基准序列模型拥有更少的参数和减少的乘积累加操作（MACs），突显了其计算效率。 相关数据、代码及模型可在[该链接](https://example.com)获取。
URL
https://arxiv.org/abs/2602.03669
