  AI-based lane detection algorithms were actively studied over the last few
years. Many have demonstrated superior performance compared with traditional
feature-based methods. The accuracy, however, is still generally in the low 80%
or high 90%, or even lower when challenging images are used. In this paper, we
propose a real-time lane detection system, called Scene Understanding
Physics-Enhanced Real-time (SUPER) algorithm. The proposed method consists of
two main modules: 1) a hierarchical semantic segmentation network as the scene
feature extractor and 2) a physics enhanced multi-lane parameter optimization
module for lane inference. We train the proposed system using heterogeneous
data from Cityscapes, Vistas and Apollo, and evaluate the performance on four
completely separate datasets (that were never seen before), including Tusimple,
Caltech, URBAN KITTI-ROAD, and X-3000. The proposed approach performs the same
or better than lane detection models already trained on the same dataset and
performs well even on datasets it was never trained on. Real-world vehicle
tests were also conducted. Preliminary test results show promising real-time
lane-detection performance compared with the Mobileye.
