CAN-bus Verileri kullanarak Agresif Sürüş Tespiti için Temel Sınıflandırma Algoritmalarının Performans Değerlendirmesi

Abstract

Detection of driver moods associated to driving style such as drowsy, distracted, vigilant, calm, or aggressive driving is one of the main problems of Advanced Driver Assistance Systems and it obviously plays vital role in the prevention of traffic accidents. The main goal of this study is to compare the performances of major Supervised Learning based Classification Algorithms (SLCAs) for aggressive driving detection, which is one of the fundamental problems for understanding driver mood or driving style through CAN (Control Area Network) bus sensor data. These algorithms utilize CAN-bus data acquired by OBDII (On-board Diagnostics) socket of the vehicle. In our experiments, to get ground truth data, many trials referring to aggressive and calm driving have been conducted by different subject drivers and these sensor data have been labeled as "aggressive" and "calm". Afterwards, these transformed into training data to assess performances of SLCAs. As a result, the Naïve Bayes Classifier has been found to be more successful than the others.