A Design of Autonomous Remote Control Car Using Convolutional Neural Network and Haar-like Features Classifier

The autonomous vehicle is a type of vehicle that can drive safely without any human intervention. This safety is related to the vehicle's capability to keep moving on its track without disturbing the other lines, detect objects in front of it, and estimate the distance to that object to prevent an accident. However, only a few researchers develop autonomous vehicles that can follow the predetermined path, detect objects, and estimate the distance to said objects. In this research, the author wants to make an autonomous remote control car with those three features. This project develops an autonomous remote control car controlled by a convolutional neural network to keep the car in a track. The device has features to classify three object classes (i.e. pedestrian, car, and stop sign) using Haar-like classifier. Besides, the device can estimate the distance to the object by using pinhole imaging theory. The device takes images from a mobile phone attached to the car as its only input and processes the images in MATLAB2019a. The final device can follow the track with the accuracy ranging from 86.67% to 100.00% and classify three object classes with the accuracy ranging from 53.33% to 86.67%. Besides, the device can estimate the object distance with average error equals to 2.43 cm