Artificial Intelligence based Self-Driving Car

Abstract

The paper proposes a self-driving car model also called autonomous, robotic or driver-less car is one that operates and navigates using its intelligence. The basic idea behind the paper is to develop a 1/10 scale RC car to portray an automated car. The model consists of the following software and hardware components such as CNN (Convolutional neural network), Monocular vision algorithm, Haar cascade classifier, Raspberry Pi Board model B+, Pi camera, Arduino, and an Ultrasonic sensor. The Pi camera and ultrasonic sensor are attached to the raspberry pi board to collect input images along with sensor data to stream these data to the server which in our case is the laptop. The (CNN) convolutional neural network running on the server will be used to enable lane detection to provide steering predictions that are left, right, forward based on the input image. The haar cascade classifier will be used to detect signals and stop sign and monocular vision algorithms to calculate distance from them. The ultrasonic sensor will be used for front collision avoidance by stopping the car at a certain distance before the obstacle ahead. The navigation commands such as right, left, forward, stop will be sent to the car through Arduino which is connected to the RC car's remote, this will make the car drive autonomously based on neural network predictions and some hard coded rules. Thus this model will enable autonomous driving by self-navigation via lane detection, stopping at detection of stop signs, red lights and moving on green signal and front collision avoidance in a cost-effective manner. Hardcoded rules, obstacle avoidance mechanisms, machine learning models, and smart object discrimination will help the system follow traffic rules and navigate the car using artificial intelligence.