A kernel-based extreme learning modeling method for speed decision making of autonomous land vehicles

This paper presents a kernel-based extreme learning machine (KELM) modeling method for speed decision making of autonomous land vehicles (ALVs) on rural roads. The model is obtained offline via the KELM algorithm using a small number of typical samples collected by an ALV platform on rural roads fro...

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Bibliographic Details
Published in2017 6th Data Driven Control and Learning Systems (DDCLS) pp. 769 - 775
Main Authors Xiangfei Wu, Xin Xu, Xiaohui Li, Kai Li, Bohan Jiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Autonomous Land Vehicles
Data models
Decision making
Kernel
Kernel-based Extreme Learning Machine
Land vehicles
Roads
Rural Roads
Speed Decision Making
Support vector machines
Training
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Summary:This paper presents a kernel-based extreme learning machine (KELM) modeling method for speed decision making of autonomous land vehicles (ALVs) on rural roads. The model is obtained offline via the KELM algorithm using a small number of typical samples collected by an ALV platform on rural roads from experienced drivers. Compared with other typical machine learning algorithms such as support vector regression and extreme learning machine, the KELM method has the advantages of fast training speed and higher modeling precision. Real-vehicle experiments have been carried out to test the model on an ALV platform on rural roads online. The experimental results demonstrate the effectiveness of the proposed speed decision-making model.
DOI:10.1109/DDCLS.2017.8068171