LSTM ağları kullanılarak ağır vasıtalar için sürücü davranışlarının sınıflandırılması

Despite growing autonomous driving trend, human is still a major factor in the current vehicle technology. Drivers have a great impact on both fuel economy and accident prevention. Therefore, identification and evaluation of driving behaviors are crucial to improve the performance, safety and energy management of vehicle technologies, particularly for heavy-duty vehicles. In this thesis, several driving behaviors with different acceleration and car following characteristics are generated on a realistic truck model in IPG's TruckMaker simulation environment. A Long Short Term Memory (LSTM) classifier is then utilized to recognize driving behaviors. First, six drivers are defined based on their longitudinal and lateral acceleration limits. The classifier is trained using driving signals acquired from the simulated truck which follows an artificial training road with different trailer loads. The training road is designed to cover possible road curves that can be seen in highways. The model is tested with driving signals that are collected from a realistic road using the same method. Then, three drivers (calm, normal and aggressive) are defined based on their longitudinal acceleration profiles in car following and the classifier is trained and tested using driving signals of these drivers in different traffic scenarios. Results show that the proposed LSTM classifier is capable of successfully capturing the dynamic relations encoded in driving signals and recognizing different driving behaviors in small time samples.