Real-Time Lane Detection and Extreme Learning Machine Based Tracking Control for Intelligent Self-driving Vehicle

被引：1

作者：

Hossain, Sabir ^{[1
]}

Doukhi, Oualid ^{[1
]}

Lee, Inseung ^{[1
]}

Lee, Deok-jin ^{[1
]}

机构：

[1] Kunsan Natl Univ, Dept Mech Engn, Gunsan, South Korea

来源：

INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 2 | 2020年 / 1038卷

基金：

新加坡国家研究基金会;

关键词：

Self-driving vehicle; OS-ELM; Kalman filter; Lane detection; Image transformation;

D O I：

10.1007/978-3-030-29513-4_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The rising self-driving technological innovations are viewed as brimming with challenges and opportunities because of its tremendous research territory. One of the challenges for the autonomous vehicle is straight and curve line detection to enhance the assistance in the autonomous characteristics. We will use a unique way of detecting a curve line algorithm in the vehicle based on the Kalman filter as well as the parabola equation model to calculate the parameters of the curve lane. For robust stability and performance, we will use an on-line sequential extreme learning machine method. We present our proposed result through the simulation study.

引用

页码：41 / 50

页数：10

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