April 2019

tl;dr: Proposed a new dataset for road detection and ego-lane detection.

The most interesting contribution of this paper is that it provided a broad review of SOTA evaluation metrics for road area and ego-lane detection.

Two groups of metrics: those operate on the perspective image directly (image representation) and those operate on BEV image (metric representation).
Segmentation based approach: pixel value evaluation, TP, FP on pixel level, IoU
Lane lines/boundaries detection: distance between GT prediction markings. By allowing a flexible margin for counting successful border candidates, TP and FP can be defined.
In general, the metric evaluation results in BEV are always lower than that in the perspective space.
Evaluation in perspective space is biased and does not reflect the actual performance at regions far away adequately.
As long as you can tell which point (either in area or in the boundary), you can define TP, FP, precision, recall, F1, IoU, AP, and the precision-recall curve.

For an ADAS warning a driver about oncoming narrow street sections, the width of the road needs to be measured in a distance of about 30-50m.
When mapped to BEV, the resolution is 10 cm x 10 cm or 5 cm x 5 cm. The x and z range are [-10m, 10] x [~6m, ~50m].

Learning-Deep-Learning

A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms