The Simulation of Autonomous Vehicle Using ROS2 Based on Convolutional Neural Networks for Object Recognition

Abstract

The main justification for implementing an Autonomous Vehicle (AV) system in the real world is the safety aspect of driving, because if there is an error in driving then the error will become a gap that can threaten the safety of the driver himself and other drivers, therefore an AV system is made to reduce driver errors. in driving. The aim of this research is to implement one of the parts of the AV system, that is object recognition, and in this study, we also conduct an experiment with simulating the object recognition feature that has been implemented in order to get more concrete results. Architectural object recognition is designed to extract key features from traffic sign images, the traffic sign detection uses the customized Convolutional Neural Networks (CNNs) architecture. After the architectural has been implemented, training will be carried out using Custom Traffic Sign Dataset and experiments will also be conducted to simulate object recognition by applying ROS2 as a car robotic system that represents a car's functionality system in the real world. the results of this study for the implementation of the modified CNNs architecture is 99.96% and the results of the simulations carried out show that the prototype can detect traffic signs objects with a distance of 10m