Abstract

Autonomous Ground Vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these process require a fast computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One can experiences difficulties because the condition of the road varies, this research proposes a combination of Gray Level Co-occurrence Matrix (GLCM) a statistical method to extract feature and Local Binary Pattern (LBP) feature to improve the robustness of the features. The kNN classifier is used to do the classification with the accuracy of 98% and 12 picture processed per second.

Highlights

  • The number of four-wheeled vehicle users in Indonesia is increasing every year and the number of accidents increases these accidents are caused by three factors, inadequate infrastructure, inadequate vehicles, and human error, human error contribute 61% to the incident counts

  • One of the features in autonomous technology is the classification of road surface types, which can be used to regulate autonomous vehicle behavior, such as regulating vehicle speed, giving an early warning system to road conditions in front of it and keeping the vehicle stay in the track

  • Another thing that is no less important is the method proposed to carry out the road classification process needs to have fast computational time because it will be implemented in real-time conditions

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Summary

Introduction

The number of four-wheeled vehicle users in Indonesia is increasing every year and the number of accidents increases these accidents are caused by three factors, inadequate infrastructure, inadequate vehicles, and human error, human error contribute 61% to the incident counts This needs to be a concern in order to improve road user safety. One of the features in autonomous technology is the classification of road surface types, which can be used to regulate autonomous vehicle behavior, such as regulating vehicle speed, giving an early warning system to road conditions in front of it and keeping the vehicle stay in the track Another thing that is no less important is the method proposed to carry out the road classification process needs to have fast computational time because it will be implemented in real-time conditions

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