| A NEW ADAPTIVE REGION OF INTEREST EXTRACTION METHOD
FOR TWO-LANE DETECTION |
| Yingfo Chen, Pak Kin Wong, Zhi-Xin Yang |
| University of Macau |
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| ABSTRACT |
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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 |
| Key Words:
Two-lane detection, adaptive region of interest, improved edge detection method, improved random sample
consensus |
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Corresponding Author.
Pak Kin Wong , Email. 
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