Self Car Driving Using Neural Networks And AI

Abstract: Self-driving cars, also referred to as autonomous vehicles (AVs), represent one of the most promising technological advancements of the 21st century. They combine artificial intelligence, computer vision, machine learning, and sensor fusion to navigate and operate without direct human intervention. The purpose of this research is to design and implement a cost-effective self-driving car prototype that can perform lane detection, obstacle avoidance, and path navigation using open-source technologies. This paper discusses the theoretical background, development methodology, experimental evaluation, results, and implications for future mobility systems. The proposed system utilizes a Raspberry Pi, camera module, and ultrasonic sensors for perception and control. Experimental results indicate lane detection accuracy of 95% and obstacle avoidance success of 90% in controlled environments. The research concludes that while current limitations prevent full autonomy, low-cost self-driving prototypes play an essential role in advancing autonomous vehicle education and research.