Makine öğrenimi aracılığıyla kendi kendine sürüşlü araçlarda şerit tespiti ve direksiyon kontrolünün geliştirilmesi

Auto lane keeping, an increasingly prevalent driver assistance technology in modern vehicles, facilitates the accurate positioning of the vehicle within road lanes, a crucial aspect for subsequent lane deviation and trajectory planning in fully autonomous vehicles. Traditional lane detection methods have historically relied on sophisticated hand-crafted features and heuristics, which, while computationally efficient, face scalability challenges due to the diverse and dynamic nature of road scenes. However, recent advancements in machine learning, particularly with Convolutional Neural Networks (CNNs), have revolutionized this field by replacing hand-crafted feature detectors with deep networks capable of learning pixel-wise lane segmentations. In this thesis, we aim to address the lane detection problem using a variety of methods. To delve deeper, we utilize a dataset comprising highway lane images to conduct a comparative analysis of two distinct methods. Initially, we employ the traditional edge-detection method, featuring hand-crafted features. Subsequently, we explore various Deep Convolutional Network (CNN) architectures tailored to tackle the lane detection challenge. Our investigation culminates in a comparative assessment of these methods, leveraging images derived from their respective outputs.