This repository is an educational book and accompanying code for learning algorithms for automated driving, specifically lane detection, vehicle control, and camera calibration. It uses the CARLA simulator to provide a realistic environment. The code is organized into exercises with provided solutions, test notebooks, and utility modules. The lane detection pipeline uses a deep learning segmentation model (trained with fastai) to detect lane boundaries in camera images, then applies inverse perspective mapping to convert pixel coordinates to road coordinates and fits polynomials to the lane boundaries. The control module implements PID for longitudinal control and pure pursuit for lateral control. The camera calibration module uses vanishing points to estimate camera yaw and pitch. The repository is primarily educational but contains well-structured, reusable components for camera geometry, geometric computations, and control algorithms. It is hybrid in nature, blending research/educational content with software components that could be extracted for other projects.