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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from   Applied Deep Learning and Computer Vision for Self-Driving Cars Build autonomous vehicles using deep neural networks and behavior-cloning techniques

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Length 332 pages
Edition 1st Edition
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Authors (3):
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Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Author Profile Icon Dr. S. Senthamilarasu
Dr. S. Senthamilarasu
Balu Nair Balu Nair
Author Profile Icon Balu Nair
Balu Nair
Sumit Ranjan Sumit Ranjan
Author Profile Icon Sumit Ranjan
Sumit Ranjan
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars FREE CHAPTER 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy
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!

You have been reading a chapter from
Applied Deep Learning and Computer Vision for Self-Driving Cars
Published in: Aug 2020
Publisher: Packt
ISBN-13: 9781838646301
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