Year 2020, Volume , Issue 20, Pages 774 - 782 2020-12-31

CAN-bus Verileri kullanarak Agresif Sürüş Tespiti için Temel Sınıflandırma Algoritmalarının Performans Değerlendirmesi
Performance Evaluation of Major Classification Algorithms for Aggressive Driving Detection using CAN-bus Data

Berat KARABULUTER [1] , Özgür KARADUMAN [2] , Murat KARABATAK [3] , Haluk EREN [4]


İleri Sürücü Destek Sistemleri (İSDS) insansız araçlar için bir kilometre taşıdır. Bu çalışmanın temel amacı, CAN-bus sensör verilerini kullanarak İSDS’ nin temel problemlerinden birisi olan agresif sürüş tespiti yapmak için temel sınıflandırma algoritmalarının performanslarını karşılaştırmaktır. Yapay Sinir Ağları, Destek Vektör Makineleri, K-Enyakın Komşular, C4.5 Algoritması ve Naïve Bayes Sınıflandırıcısının yer aldığı bu çalışmada Eğiticili Öğrenme Tabanlı Sınıflandırma Algoritmaları (EÖSAs) kullanılmıştır. Bu algoritmalar, sürücünün ruh halini belirlemek amacıyla aracın OBDII soketinden elde edilen CAN-bus verilerini kullanır. Deneylerimizde, referans olacak ham verileri elde etmek için, farklı denekler tarafından agresif ve sakin sürüş gerçekleştirilerek elde edilen CAN-bus sensör verileri "agresif" ve "sakin" olarak etiketlenmiştir. Daha sonra bu veriler, sözkonusu sınıflandırma algoritmalarının yapısına uygun hale gelmesi için normalize edilmiştir. İşlemin sonunda, sonraki ve önceki adımlar birleştirilerek, sürücü ruh halini tespit etmek için EÖSA’ ların performansını değerlendirmek üzere eğitim verilerine dönüştürülmüştür. Belirtilen algoritmalar için yapılan performans değerlendirmesi sonucunda, Naïve Bayes sınıflandırıcısının diğerlerinden daha başarılı olduğu görülmüştür.
Advanced Driving Assistants Systems (ADAS) have an important milestone for unmanned vehicles. The main goal of this study is to compare the performances of major classification algorithms for aggressive driving detection, which is one of the fundamental problems of ADAS, through CAN (Control Area Network) bus sensor data. Supervised Learning based Classification Algorithms (SLCAs) are employed by this study, which are Artificial Neural Networks (ANN), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), C4.5 Algorithm (J48), and Naïve Bayesian Classifier. These algorithms utilize CAN bus (Controller Area Network Bus) data acquired by OBDII (On-board Diagnostics) socket of the vehicle to detect driver mood associated with driving style. With the aim of ground truth, aggressive and calm drive were tried by different subject drivers, and acquired CAN bus sensor data in question is labeled as "aggressive" and "calm”, in our experiments. Afterwards, these are normalized for proper modality in mentioned classification algorithms. In the end of the process, combining latter and former steps are transformed into training data to assess performances of SLCAs for driver mood detection. Resultant performance evaluation for the algorithms suggest that the Naïve Bayes Classifier is more successful than the others.
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Primary Language en
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-8712-3382
Author: Berat KARABULUTER
Institution: FIRAT UNIVERSITY
Country: Turkey


Orcid: 0000-0002-6569-3616
Author: Özgür KARADUMAN (Primary Author)
Institution: FIRAT UNIVERSITY
Country: Turkey


Orcid: 0000-0002-6719-7421
Author: Murat KARABATAK
Institution: FIRAT UNIVERSITY
Country: Turkey


Orcid: 0000-0002-4615-5783
Author: Haluk EREN
Institution: FIRAT UNIVERSITY
Country: Turkey


Dates

Publication Date : December 31, 2020

APA Karabuluter, B , Karaduman, Ö , Karabatak, M , Eren, H . (2020). Performance Evaluation of Major Classification Algorithms for Aggressive Driving Detection using CAN-bus Data . Avrupa Bilim ve Teknoloji Dergisi , (20) , 774-782 . DOI: 10.31590/ejosat.743076