Lane marker detection is a crucial component of the autonomous driving and driver assistance systems. Modern deep lane detection methods with row-based lane representation exhibit excellent performance on lane detection benchmarks. Through preliminary oracle experiments, we firstly disentangle the lane representation components to determine the direction of our approach. We show that correct lane positions are already among the predictions of an existing row-based detector, and the confidence scores that accurately represent intersection-over-union (IoU) with ground truths are the most beneficial. Based on the finding, we propose LaneIoU that better correlates with the metric, by taking the local lane angles into consideration. We develop a novel detector coined CLRerNet featuring LaneIoU for the target assignment cost and loss functions aiming at the improved quality of confidence scores. Through careful and fair benchmark including cross validation, we demonstrate that CLRerNet outperforms the state-of-the-art by a large margin - enjoying F1 score of 81.43% compared with 80.47% of the existing method on CULane, and 86.47% compared with 86.10% on CurveLanes.

翻译：车道标记检测是自动驾驶和驾驶辅助系统中的关键组成部分。基于行式车道表征的现代深度车道检测方法在车道检测基准上表现出色。通过初步的预言实验，我们首先解构了车道表征组件，以确定我们方法的研究方向。研究表明，现有行式检测器的预测中已包含正确的车道位置，而能够准确反映与真实值之间交并比（IoU）的置信度分数最为有益。基于这一发现，我们提出了LaneIoU，该方法通过考虑局部车道角度，更好地与评估指标相关联。我们开发了一种名为CLRerNet的新型检测器，将LaneIoU用于目标分配代价和损失函数，旨在提升置信度分数的质量。通过包括交叉验证在内的细致公平基准测试，我们证明CLRerNet大幅超越了现有最先进方法——在CULane上F1分数达81.43%（现有方法为80.47%），在CurveLanes上达86.47%（现有方法为86.10%）。