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

Benefits of SDCs

Indeed, some people may be afraid of autonomous driving, but it is hard to deny its benefits. Let's explore a few of the benefits of autonomous vehicles:

  • Greater safety on roads: Government data identifies that a driver's error is the cause of 94% of crashes (https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115). Higher levels of autonomy can reduce accidents by eliminating driver errors. The most significant outcome of autonomous driving could be to reduce the devastation caused by unsafe driving, and in particular, driving under the influence of drugs or alcohol. Furthermore, it could reduce the heightened risks for unbelted occupants of vehicles, vehicles traveling at higher speeds, and distractions that affect human drivers. SDCs will address these issues and increase safety, which we will see in more detail in the Levels of autonomy section of this chapter.
  • Greater independence for those with mobility problems: Full automation generally offers us more personal freedom. People with special needs, particularly those with mobility limitations, will be more self-reliant. People with limited vision who may be unable to drive themselves will be able to access the freedom afforded by motorized transport. These vehicles can also play an essential role in enhancing the independence of senior citizens. Furthermore, mobility will also become more affordable for people who cannot afford it as ride-sharing will reduce personal transportation costs.
  • Reduced congestion: Using SDCs could address several causes of traffic congestion. Fewer accidents will mean fewer backups on the highway. More efficient, safer distances between vehicles and a reduction in the number of stop-and-go waves will reduce the overall congestion on the road.
  • Reduced environmental impact: Most autonomous vehicles are designed to be fully electric, which is why the autonomous vehicle has the potential to reduce fuel consumption and carbon emissions, which will save fuel and reduce greenhouse gas emissions from unnecessary engine idling.

There are, however, potential disadvantages to SDCs: 

  • The loss of vehicle-driving jobs in the transport industry as a direct impact of the widespread adoption of automated vehicles. 
  • Loss of privacy due to the location and position of an SDC being integrated into an interface. If it can be accessed by other people, then it can be misused for any crime.
  • A risk of automotive hacking, particularly when vehicles communicate with each other.
  • The risk of terrorist attacks also exists; there is a real possibility of SDCs, charged with explosives, being used as remote car bombs.

Despite these disadvantages, automobile companies, along with governments, need to come up with solutions to the aforementioned issues before we can have fully automated cars on the roads.