Lane Following Car using Raspberry Pi Model

Self-driving automobiles are autos that can find their way to a location on their own. Many well-known companies and developers have made significant investments in this area and developed their self-driving vehicle systems. This work, which intends to create a self-driving platform, was motivated by the exciting field of self-driving cars. This article proposes a working self-driving car prototype using a Raspberry Pi, an Arduino Uno, and a camera-based system. The three main modules used by the car are lane detection, obstacle detection, and traffic sign detection. A camera module that is mounted on the live stream images are recorded by the car's roof, sent to the Raspberry Pi for processing, and then sent to all three modules. For lane detection, algorithms like Canny Edge Detection and the Hough Transform are used. The car predicts the direction it wishes to go in based on the output of these algorithms. With the help of CNN and OpenCV, the Traffic Sign Detection Module recognizes traffic signs. The HAAR Cascade approach is used by obstacle detection to identify potential roadside obstacles including cars and pedestrians.