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A New Adaptive Region of Interest Extraction Method for Two-Lane Detection

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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.

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Abbreviations

R(i, j) G(i, j) B(i, j):

gray values of pixels

ψ :

roll angle

θ :

pitch angle

ϕ :

yaw angle

R:

rotation matrix

t:

translation vector

P :

pavement on road

L :

lane on road

O :

objects on road

U :

undefined things on road

μ IL :

mean deviation of type L

σ IL :

standard deviation of type L

L xy :

likelihood function

CNN:

Convolutional Neural Network

DBSCAN:

Density Based Spatial Clustering of Applications with Noise

α L :

quadratic coefficient of the left lane line

α R :

quadratic coefficient of the right lane line

α T :

threshold value for curve lane

α T1 :

threshold for undesirable curves

α T2 :

threshold for unexpected straight lines

α T3 :

threshold of strength for straight lines

L S(i) :

strength information of the ith line

P M(i) :

number of feature points in the ith line

L LM(i) :

length of the ith line

P M :

number of feature points of the line containing the most feature points

α P1, α P2 :

thresholds for judging the distance between two feature points

MPC:

Model Predictive Control

HT:

Hough Transform

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Acknowledgement

This work was funded by the University of Macau (File nos. MYRG2019-00028-FST and MYRG2019-0137-FST), Natural Science Foundation of Guangdong Province of China (File no. 2019A1515011602 (EF009/FST-WPK/2019/DSTGP)) and the Science and Technology Development Fund of Macau SAR (File nos. SKL-IOTSC-2011-2023, 0018/2019/AKP and 0008/2019/AGJ). The authors would also like to thank Dr. Ahmadi Ghadikolaei Meisam for his support of proofreading.

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Authors and Affiliations

  1. Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau, Zhuhai, 999078, China

    Yingfo Chen & Pak Kin Wong

  2. State Key Laboratory of Internet of Things for Smart City, Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau, Zhuhai, 999078, China

    Zhi-Xin Yang

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  1. Yingfo Chen
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Correspondence to Pak Kin Wong.

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Chen, Y., Wong, P.K. & Yang, ZX. A New Adaptive Region of Interest Extraction Method for Two-Lane Detection. Int.J Automot. Technol. 22, 1631–1649 (2021). https://doi.org/10.1007/s12239-021-0141-0

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