Video Oyunu Ortamında Otonom Sürüş İçin Şerit Tespiti

To ensure comfortable and safe driving, the automotive industry has accelerated the development of autonomous vehicles in recent years. In the design of autonomous vehicles, challenging problems such as lane detection need to be solved. Convolutional neural networks, which show superior performance in many fields, have also been used in the lane detection problem. The datasets required to train CNN models are too large to be collected and labeled by manual effort. In this study, a method is proposed to automatically collect a labeled data set from the video game environment to be used in the detection of highway lanes. Different CNN models such as ResNet50, VGG16, Xception, and InceptionV3 networks are trained using the Transfer Learning method with 745,823 collected images. The images captured by the front vehicle camera are used as input, the coordinates of the points in the left and right lane and the center of the lane in the 2D plane in front of the vehicle and the angle of the vehicle are used as outputs. The performances of these models are tested and compared on the images collected from a road not used in the training set. According to the performance comparisons, ResNet50 performs best.