@article{ref1,
title="A new adaptive region of interest extraction method for two-lane detection",
journal="International journal of automotive technology",
year="2021",
author="Chen, Yingfo and Wong, Pak Kin and Yang, Zhi-Xin",
volume="22",
number="6",
pages="1631-1649",
abstract="As a key environment perception technology of autonomous driving or driver assistance systems, lane detection is to ensure vehicles to drive safely in corresponding lane. However, existing lane detection algorithms for two-lane detection focus on using various filtering methods to reduce the impact of useless information, resulting in low accuracy and low efficiency. In this paper, a novel Adaptive Region of Interest (A-ROI) extraction method is proposed to improve the accuracy and real-time performance of the two-lane detection algorithm. Three key technologies are introduced to solve the problems. First, A-ROI, which only focuses on the lane where the vehicle is located, is applied to the Bird's-Eye-View image obtained by using Inverse Perspective Mapping (IPM). Next, based on Bayesian framework and Likelihood models, a lane feature extraction method with a lane-like feature filter is used for edge detection. Finally, an improved Random Sample Consensus (RANSAC) algorithm is introduced by using a filter that can remove noisy lane data. The performance of the proposed A-ROI method together with the improved lane detection method is evaluated via simulation of various scenarios. Experimental results show the proposed method has better accuracy and real-time performance than the traditional lane detection methods.<p /> <p>Language: en</p>",
language="en",
issn="1229-9138",
doi="10.1007/s12239-021-0141-0",
url="http://dx.doi.org/10.1007/s12239-021-0141-0"
}