Akıllı Yol Durum Sensörü Tasarımı

Year 2019, Volume: 11 Issue: 1, 396 - 401, 31.01.2019

Fecir Duran , Mustafa Teke

https://doi.org/10.29137/umagd.510777

Cited By: 5

Abstract

Bu çalışmada, yol yüzeyinin durumunu tespit eden akıllı yol durum sensörü tasarlanmıştır. Sürücülerin ve yolcuların güvenliğini en çok tehlikeye sokan buzlu yol durumunun tespiti üzerine çalışılmıştır. Yol yüzeyi tahmini için toprak sıcaklığı, hava sıcaklığı, hissedilen nem, hava basıncı ve yol yüzeyindeki iletkenlik değerleri sınıflandırma algoritmalarında öznitelik olarak seçilmiştir. Yol yüzeyi buzlu, kuru, ıslak ve tuzlu-ıslak olarak sınıflandırılmıştır. Sınıflandırma algoritmaları olarak K en yakın komşu ve Destek Vektör Makinası tercih edilmiştir. K en yakın komşu algoritmasının, Destek Vektör Makinası algoritmasına göre daha iyi sonuç verdiği görülmüştür. Sınıflandırıcı tek kartlı bilgisayar olarak bilinen Raspberry Pi3 üzerinde gerçek zamanlı olarak çalıştırılmaktadır. Tasarlanan yol durum sensörü mevcut sensörlere göre kurulumu kolay ve yüksek başarıma sahiptir.

Keywords

Makine Öğrenmesi, Yol Durum Sensörü, Gömülü Sistem

Intelligent Road Condition Sensor Design

Abstract

In this study, intelligent road condition sensor is designed to determine the condition of the road surface. It has been studied on the determination of the icy road situation which endangers the safety of drivers and passengers at most. For road surface estimation, soil temperature, air temperature, sensed humidity, air pressure and conductivity values on the road surface are selected as attributes in classification algorithms. The road surface is classified as icy, dry, wet and salty-wet. K-Near Neigbours and Support Vector Machine were preferred as classification algorithms. It is seen that K-Near Neigbours algorithm has given more accurate results than Support Vector Machine algorithm. The classifier is run in real time on the Raspberry Pi3, known as a single board computer. The designed road condition sensor is easy to install and has high performance in comparison with existing sensors.

Keywords

Machine learning, Road Condition Sensor, Embedded System

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