  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.
