Training eines Neuronalen Netzes (Yolov8) zur Echtzeiterkennung von Autos in Airsim

A YOLOv8 neural network was trained to detect cars in real time within the AirSim Unreal Engine simulation environment. Successive expansions of the training set, image augmentations, and validation on both public and simulated images were applied, raising the mean average precision from approximately 0.31 to 0.66. The resulting model delivers reliable car detection while remaining lightweight enough for use on modest hardware, supporting further research on autonomous‑vehicle technologies.