Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu
Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

By: Sumit Ranjan, Dr. S. Senthamilarasu

Overview of this book

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.
Table of Contents (18 chapters)
1
Section 1: Deep Learning Foundation and SDC Basics
5
Section 2: Deep Learning and Computer Vision Techniques for SDC
10
Section 3: Semantic Segmentation for Self-Driving Cars
13
Section 4: Advanced Implementations
The Foundation of Self-Driving Cars

The driverless car is popularly known as an self-driving car (SDC), an autonomous vehicle, or a robot car. The purpose of an autonomous car is to drive automatically without a driver. The SDC is the sleeping giant that might improve everything from road safety to universal mobility, while dramatically reducing the costs of driving. According to McKinsey & Company, the widespread use of robotic cars in the US could save up to $180 billion annually in healthcare and automotive maintenance alone based on a realistic estimate of a 90% reduction in crash rates. 

Although self-driving automotive technologies have been in development for many decades, it is only in recent years that breakthroughs have been achieved. SDCs have proved to be much safer than human drivers, and automotive firms as well as other tech firms are investing billions in bringing this technology into the real world. They struggle to find great engineers to contribute to the field. This book will teach you what you need to know to kick-start a career in the autonomous driving industry. Whether you're coming from academia, or from within the industry, this book will provide you with the foundational knowledge and practical skills you will need to help build a future with Advanced Driver-Assistance Systems (ADAS) engineers or SDC engineers. Throughout this book, you will study real-world data and scenarios from recent research in autonomous cars.

This book can also help you learn and implement the state-of-the-art technologies for computer vision that are currently used in the automotive industry in the real world. By the end of this book, you will be familiar with different deep learning and computer vision techniques for SDCs. We'll finish this book off with six projects that will give you a detailed insight into various real-world issues that are important to SDC engineers.

In this chapter, we will cover the following topics:

  • Introduction to SDCs
  • Advancement in SDCs
  • Levels of autonomy
  • Deep learning and computer vision approaches for SDCs

Let's get started!