Comparison of Lane Detection Algorithms for ADAS Using Embedded Hardware Architectures

Abstract

Fast and robust lane detection algorithms are a fundamental technology for the development of advanced driver assistant systems (ADAS). Many projects in science and industry are using these kinds of algorithms. Unfortunately, algorithm implementations mainly focus on standard PC based hardware. If and how the processing can be realized on embedded devices in real-time is often not considered. Therefore, in this paper we present an extended evaluation of different optical based lane detection algorithms regarding both functional quality, and execution time on embedded devices. We compared five different lane detection algorithms for curved roads in combination with four different feature extraction filters. While the functional evaluation will be done by utilizing the F-measure metric, the execution time will be measured directly on embedded hardware. Furthermore, the algorithms were optimized to allow real-time processing. Our results show, that lane detection on images with a resolution of $1242 \times 375$ pixels can be done with up to 54 frames per second (fps) on an embedded ARM Cortex-A53 processor running at 1200 MHz.