SUPER A Novel Lane Detection System

Abstract

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 lanedetection performance compared with the Mobileye.