Self Driving Car

When human drivers let intelligent software take the wheel: the beginning of a new era in personal mobility. “Smart, wide-ranging, [and] nontechnical.” —Los Angeles Times “Anyone who wants to understand what's coming must read this fascinating book.” —Martin Ford, New York Times bestselling author of Rise of the Robots In the year 2014, Google fired a shot heard all the way to Detroit. Google's newest driverless car had no steering wheel and no brakes. The message was clear: cars of the future will be born fully autonomous, with no human driver needed. In the coming decade, self-driving cars will hit the streets, rearranging established industries and reshaping cities, giving us new choices in where we live and how we work and play. In this book, Hod Lipson and Melba Kurman offer readers insight into the risks and benefits of driverless cars and a lucid and engaging explanation of the enabling technology. Recent advances in software and robotics are toppling long-standing technological barriers that for decades have confined self-driving cars to the realm of fantasy. A new kind of artificial intelligence software called deep learning gives cars rapid and accurate visual perception. Human drivers can relax and take their eyes off the road. When human drivers let intelligent software take the wheel, driverless cars will offer billions of people all over the world a safer, cleaner, and more convenient mode of transportation. Although the technology is nearly ready, car companies and policy makers may not be. The authors make a compelling case for why government, industry, and consumers need to work together to make the development of driverless cars our society's next “Apollo moment.”

Much of North American society has been built around automobiles -- our cities are designed around them; our economy is propelled by them. Therefore, the impact and benefit of autonomous vehicles to people, to the environment, and to society as a whole will be profound as they become more commonplace. This idea once seemed futuristic and far-fetched, but every day we come closer to realizing such technology in our daily lives. Proper automation will allow vehicles to move more quickly and safely by removing the risk of human error, and the law will need to adapt to this new reality. Civil liability may shift from drivers to manufacturers. Criminal acts will be changed by a "new normal" surrounding criminal intent. How would all of this be affected, for example, by a person's decision to take control of the vehicle rather than rely on automation? What if the person relies only on automation? Both are potentially risky. Over the coming decades, the law surrounding motor vehicles is going to go through profound changes as autonomous vehicles become common and issues of law deriving from advances in technology inevitably arise. The definition of autonomywill be debated as we move to find new solutions to age-old problems, ranging from gridlock to human error. With the transition to, and ongoing evolution of, autonomous vehicles, the law will have to be modified accordingly. A new area of law will be needed, and with it, an assessment of how the current law can be adapted. Autonomous Vehicles: Self-Driving Cars and the Law of Canadawill serve as a ready resource as courts and litigants begin the journey down this new road.

This book aims to teach the core concepts that make Self-driving vehicles (SDVs) possible. It is aimed at people who want to get their teeth into self-driving vehicle technology, by providing genuine technical insights where other books just skim the surface. The book tackles everything from sensors and perception to functional safety and cybersecurity. It also passes on some practical know-how and discusses concrete SDV applications, along with a discussion of where this technology is heading. It will serve as a good starting point for software developers or professional engineers who are eager to pursue a career in this exciting field and want to learn more about the basics of SDV algorithms. Likewise, academic researchers, technology enthusiasts, and journalists will also find the book useful. Key Features: Offers a comprehensive technological walk-through of what really matters in SDV development: from hardware, software, to functional safety and cybersecurity Written by an active practitioner with extensive experience in series development and research in the fields of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Covers theoretical fundamentals of state-of-the-art SLAM, multi-sensor data fusion, and other SDV algorithms. Includes practical information and hands-on material with Robot Operating System (ROS) and Open Source Car Control (OSCC). Provides an overview of the strategies, trends, and applications which companies are pursuing in this field at present as well as other technical insights from the industry.

"Describes the invention and development of Self Driving Cars. Explores trials and tribulations along with the technological advances seen today. Includes glossary, websites, and bibliography for further reading"--Provided by publisher.

"Author Fallon presents a history of how the technology used in self-driving cars has developed, identifies recent technological gains, and surveys recent controversies surrounding the potential mass adoption of self-driving cars."--Provided by publisher.

A penetrating look at near-future disruption as truly autonomous vehicles arrive. For decades we have dreamed of building an automobile that can drive itself. But as that dream of autonomy draws close, we are discovering that the driverless car is a red herring. When self-driving technology infects buses, bikes, delivery vans, and even buildings…a wild, woollier, future awaits. Technology will transform life behind the wheel into a high-def video game that makes our ride safer, smoother, and more efficient. Meanwhile, autonomous vehicles will turbocharge our appetite for the instant delivery of goods, making the future as much about moving things as it is about moving people. Giant corporations will link the automated machines that move us to the cloud, raising concerns about mobility monopolies and privatization of streets and sidewalks. The pace of our daily lives and the fabric of our cities and towns will change dramatically as automated vehicles reprogram the way we work, shop, and play. Ghost Road is both a beacon and a warning; it explains where we might be headed together in driverless vehicles, and the choices we must make as societies and individuals to shape that future.

Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key FeaturesBuild and train powerful neural network models to build an autonomous carImplement computer vision, deep learning, and AI techniques to create automotive algorithmsOvercome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook Description 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 learnImplement deep neural network from scratch using the Keras libraryUnderstand the importance of deep learning in self-driving carsGet to grips with feature extraction techniques in image processing using the OpenCV libraryDesign a software pipeline that detects lane lines in videosImplement a convolutional neural network (CNN) image classifier for traffic signal signsTrain and test neural networks for behavioral-cloning by driving a car in a virtual simulatorDiscover various state-of-the-art semantic segmentation and object detection architecturesWho this book is for If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.

Cars have come a long way thanks to technology, from Model T cars to cars that drive themselves. In Self-Driving Cars in the Disruptors in Tech series, readers will discover how autonomous driving technology has and continues to disrupt industries from car manufacturers to city infrastructures. Series includes a table of contents, tech-forward sidebars, a timeline, glossary, index, and author biography.