Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques

English | 2020 | ISBN: 978-1838646301 | 332 Pages | PDF, EPUB | 371 MB


Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV
Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists’ focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars.
Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you’ll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.
By the end of this book, you’ll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.
What you will learn

  • Implement deep neural network from scratch using the Keras library
  • Understand the importance of deep learning in self-driving cars
  • Get to grips with feature extraction techniques in image processing using the OpenCV library
  • Design a software pipeline that detects lane lines in videos
  • Implement a convolutional neural network (CNN) image classifier for traffic signal signs
  • Train and test neural networks for behavioral-cloning by driving a car in a virtual simulator
  • Discover various state-of-the-art semantic segmentation and object detection architectures