Running control software on limited computing resources is considered one of the toughest problems. In this study, an autonomous driving software has been developed that can safely complete the map by tracking the lanes and avoiding obstacles on a robot vehicle with limited hardware components. The data was simplified with the image processing technique and the neural network was trained. Overfitting was prevented by hyperparameter tuning and synthetic data augmentation. In order to avoid obstacles, optical flow was calculated by detecting corners every 4 seconds and was used to find the focus of expansion of the vehicle. Time-to-collision was found with the FOE and the distance between the previous position and the current position of the detected point. Optimization was made by averaging the values of close points. The balance mechanism was created according to the TTC difference calculated on the right and left parts of the vehicle.