Towards a Driverless Future: Image Processing and RF Based Communication Technologies in Autonomous Vehicles

In recent years, the automotive industry has entered a revolutionary new era with the rise of electric and autonomous vehicles. This development is expected to change not only the functioning of vehicles but also lifestyles and the overall dynamics of society. Image processing and artificial intelligence applications used in autonomous vehicles can work together to ensure the safety of passengers and all traffic participants. In this study, the enabling technologies for autonomous vehicles to operate safely and efficiently are proposed and their performances are presented. In this context, the study presents a road lane detection algorithm that employs methods such as Gaussian blur, Canny edge detection, and Hough transform. This process enhances driving safety by allowing vehicles to recognize and follow lanes on the road. Additionally, this study implements a traffic sign detection and classification application using a deep learning model suitable for real life conditions. In order to improve the safety of driving experience by ensuring vehicles recognize traffic signs and react appropriately, an RF based communication system is also proposed and implemented. The results show that an accuracy value of 97.25% is achieved in our road lane detection application, and a recall value of 92.7% is obtained in the traffic sign detection model trained on our dataset.