Embarking on the Autonomous Journey: A Strikingly Engineered Car Control System Design

This thesis develops an autonomous car control system with Raspberry Pi. Two predictive models are implemented: a convolutional neural network (CNN) using machine learning and an input-based decision tree model using sensor data. The Raspberry Module controls the car hardware and acquires real-time camera data with OpenCV. A dedicated web server and event stream processor process data in real-time using the trained neural network model, facilitating real-time decision-making. Unity and Meta Quest 2 VR set create the VR interface, while a generic DIY kit from Amazon and Raspberry PI provide the car hardware inputs. This research demonstrates the potential of VR in automotive communication, enhancing autonomous car testing and user experience.