Design and Development of an Autonomous Car using Object Detection with YOLOv4

Abstract

Future cars are anticipated to be driverless point-to-point transportation services capable of avoiding fatalities To achieve this goal auto-manufacturers have been investing to realize the potential autonomous driving In this regard we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering on a track made of colored cones This paper goes through the process of fabricating a model vehicle from its embedded hardware platform to the end-to-end ML pipeline necessary for automated data acquisition and model-training thereby allowing a Deep Learning model to derive input from the hardware platform to control the car s movements This guides the car autonomously and adapts well to real-time tracks without manual feature-extraction This paper presents a Computer Vision model that learns from video data and involves Image Processing Augmentation Behavioral Cloning and a Convolutional Neural Network model The Darknet architecture is used to detect objects through a video segment and convert it into a 3D navigable path Finally the paper touches upon the conclusion results and scope of future improvement in the technique used