ML kullanarak gelişmiş sürücü destek sistemleri (ADAS) ile geliştirilmiş otonom bir aracın tasarımı ve uygulanması

This thesis discusses the design and implementation of an autonomous vehicle enhanced with advance driver assistance systems (ADAS) using machine learning. This vehicle can be classified as an educational platform suitable for researchers and specialists in the field of autonomous vehicles. Its structure can be easily modified to meet the needs of researchers, and it can be reprogrammed with ease. The work details the construction of the vehicle, including the chassis structure, suspension system, steering system, brakes, and the anti-lock braking system (ABS). Control of the vehicle is achieved through a mobile phone using a control program developed with MIT App Inventor, allowing wireless Bluetooth communication for driving. The thesis also covers the vehicle's key tasks, such as path planning and navigation using a specialized algorithm for selecting the shortest path to the destination. The vehicle is equipped with ultrasonic sensors distributed around it to detect both stationary and moving obstacles. Additionally, a LIDAR sensor is used for obstacle detection. A machine learning model is created to sense obstacles, trained on data collected from various sensors and scenario, and used to implement autonomous driving in simulation and augmented reality. The results demonstrate the vehicle's ability to navigate obstacles during its journey. Finally, the vehicle can recognize different traffic signs, trained using machine learning on a dataset of over 50,000 samples of 43 classes of German traffic signs. The model is tested for visualization and through the vehicle's camera, enabling it to recognize and respond to all traffic signs appropriately.