openThis 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.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
