After playing with lane lines detection and traffic signs recognition, it's time to feed a Deep Neural Network with a video recorded during human driving. The model will be trained afterwards to act "humanlike" and predict correct steering angles while driving autonomously. Such procedure is also called Behavioral Cloning. For simplicity (and public safety 🙂 ) I will collect video data and test the resulting DNN in a game-like simulator developed by Udacity. This project stands as a third assignment in "Self-Driving Car Engineer" course.
Tag: Deep Neural Networks
I'm going to describe a complete pipeline for Traffic Sign Recognition problem posed in Udacity course "Self-Driving Cars Engineer". Traffic Sign Recognition is a basic, day by day task for self-driving cars. That's why it has to be covered in the series about Self-Driving Cars where I present different projects related to this field. The recognition system processes a traffic sign image extracted from the road scene. Eventually, it should classify that sign into one of 43 categories. In order to make it happen, a Convolutional Neural Network is applied, being trained with 50.000 images beforehand.