Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2016
Öğrenci: ÖMÜRCAN KUMTEPE
Danışman: GÖZDE AKAR
Özet:Every year a vast number of traffic accidents occur globally. These traffic accidents cause fatalities, severe injuries and huge economical cost. Most of these traffic accidents occur due to aggressive driving behaviour. Therefore, detection of driver aggressiveness could help reducing the number of traffic accidents by warning related authorities to take necessary precautions. Although aggressiveness is a psychological phenomenon, driver aggressiveness can be analysed by monitoring certain driving behaviour such as abrupt lane changes, unsafe following distance and excess acceleration and deceleration. In this thesis work, a method is introduced in order to detect aggressive driving behaviour using a system on vehicle. The proposed method is based on fusion of visual and other sensor information to characterize related driving session and to decide whether the session involves aggressive driving behaviour. Visual information is used to detect road lines and vehicle images; whereas CAN bus information provides certain driving data such as vehicle speed and engine speed. Both information is used to obtain feature vectors which represent a driving session. These feature vectors are obtained by modelling time series data by Gaussian distributions. An SVM classifier is utilized to classify the feature vectors in order for aggressiveness decision. The proposed system is tested by real traffic data and it achieved an aggressive driving detection rate of 94.0%.