Autonomous Self Driving Car

Abstract

This paper presents the design and implementation of an autonomous self-driving car system utilizing a combination of Raspberry Pi, Arduino Uno, and various auxiliary components. The system integrates a Raspberry Pi single-board computer with a camera module, an L298N motor driver, an Arduino Uno microcontroller, an indicator controller, and a DC power supply rated at 11.2 volts.The Raspberry Pi serves as the central processing unit for the autonomous driving functionalities. It utilizes computer vision algorithms, leveraging the camera feed to analyze the car's surroundings, identify obstacles, detect lane markings, and make navigational decisions in real-time. OpenCV and PiCamera libraries are employed for image processing and camera interfacing, respectively.The L298N motor driver facilitates motor control for the car's propulsion system. The Raspberry Pi communicates with the motor driver to regulate the speed and direction of the motors based on the detected environmental cues. This enables the autonomous vehicle to maneuver autonomously through its environment. Additionally, an Arduino Uno microcontroller is integrated into the system to manage peripheral devices, such as indicator controllers. The Arduino Uno receives commands from the Raspberry Pi and orchestrates the activation of indicators or other auxiliary components to signal the vehicle's intentions to nearby pedestrians and vehicles. The DC power supply provides the necessary voltage and current to drive the motors and power the electronic components of the autonomous car system. Adequate voltage regulation and power management strategies are implemented to ensure stable operation and prevent damage to sensitive components. Through the integration of these components and the development of sophisticated software algorithms, the autonomous self-driving car system offers a promising platform for research, experimentation, and educational purposes in the field of autonomous vehicle technology. The project contributes to advancing the understanding and implementation of autonomous driving systems using readily available hardware components and open-source software resources