How to Build Self-Driving Cars From Scratch, Part 2 PDF Download
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Author: Bolakale Aremu
Publisher: AB Publisher LLC
ISBN:
Category : Computers
Languages : en
Pages : 93
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Book Description
This is part 2 of my 3-part training guide on how to build self-driving cars from scratch. This guide is bundled with a repository containing simulations, python scripts, graphics, and other useful assets. In this step-by-step guide, I teach you how to make an app that you can use to create a simulation where cars learn how to drive autonomously over racing tracks. Here’s a break down of the contents of this guide. Part 1: Car mechanics. In this part, you’ll learn how to draw the car and control it with the keyboard. You will use a multimedia library for Python called Pyglet. (https://pyglet.org/). This is the only library you will use in this guide. This is a cross-platform windowing and multimedia library for Python. It’s a powerful yet easy-to-use Python library for building games and other visually rich applications on Windows, macOS, and Linux. Part 2:Neural network and genetic algorithm. You’ll learn how to create the AI where you combine a neural network and genetic algorithm. You’ll learn how to add sensors to the car and get output from them. To prevent the untrained network from car crashes, a genetic algorithm will be used to train the cars. This will help the cars to drive simple tracks. Part 3: Challenges. You’ll add some challenges to the system. Tracks get more complicated and will take advantage from the previous track training by storing and retrieving the car brains. By the end of this training, you will have created self-driving cars that are capable of driving on unknown tracks by understanding how to steer, accelerate, and brake based on what cars see in front of them. Since autonomous cars need a brain of some kind, you know we need some AI (artificial intelligence). AI comes in many forms, but in this guide, you’ll use a neural network where the weights are adjusted by a genetic algorithm. Employment opportunities often come from work samples and concrete skills, rather than a college degree. So, you need to learn the practical aspect well enough. This guide will not only help you learn well and build a stunning portfolio, it will also provide you continuous help and support. With this book and my dedicated 24/7 help and support team, there’s nothing for you to fear. I have helped many Python developers update their automation development skills, launch successful careers and get hired for remote jobs. I notice that even the most ambitious beginners can run into problems, such as unable to decide where to begin. Sometimes they get completely lost on the way and therefore need further help. In Chapter 13, I explain how to download my repository which contains all updates of the Python scripts (codes) and simulations used in this Part 2 of the guide. Although I explain all the codes used in this guide clearly, if you need further help, just use my support link at the end of the Chapter. The truth is everyone needs help at one point or the other to learn and build automation in their development journey. I can give you more challenges and their solutions in my subsequent trainings.
Author: Bolakale Aremu
Publisher: AB Publisher LLC
ISBN:
Category : Computers
Languages : en
Pages : 93
Get Book Here
Book Description
This is part 2 of my 3-part training guide on how to build self-driving cars from scratch. This guide is bundled with a repository containing simulations, python scripts, graphics, and other useful assets. In this step-by-step guide, I teach you how to make an app that you can use to create a simulation where cars learn how to drive autonomously over racing tracks. Here’s a break down of the contents of this guide. Part 1: Car mechanics. In this part, you’ll learn how to draw the car and control it with the keyboard. You will use a multimedia library for Python called Pyglet. (https://pyglet.org/). This is the only library you will use in this guide. This is a cross-platform windowing and multimedia library for Python. It’s a powerful yet easy-to-use Python library for building games and other visually rich applications on Windows, macOS, and Linux. Part 2:Neural network and genetic algorithm. You’ll learn how to create the AI where you combine a neural network and genetic algorithm. You’ll learn how to add sensors to the car and get output from them. To prevent the untrained network from car crashes, a genetic algorithm will be used to train the cars. This will help the cars to drive simple tracks. Part 3: Challenges. You’ll add some challenges to the system. Tracks get more complicated and will take advantage from the previous track training by storing and retrieving the car brains. By the end of this training, you will have created self-driving cars that are capable of driving on unknown tracks by understanding how to steer, accelerate, and brake based on what cars see in front of them. Since autonomous cars need a brain of some kind, you know we need some AI (artificial intelligence). AI comes in many forms, but in this guide, you’ll use a neural network where the weights are adjusted by a genetic algorithm. Employment opportunities often come from work samples and concrete skills, rather than a college degree. So, you need to learn the practical aspect well enough. This guide will not only help you learn well and build a stunning portfolio, it will also provide you continuous help and support. With this book and my dedicated 24/7 help and support team, there’s nothing for you to fear. I have helped many Python developers update their automation development skills, launch successful careers and get hired for remote jobs. I notice that even the most ambitious beginners can run into problems, such as unable to decide where to begin. Sometimes they get completely lost on the way and therefore need further help. In Chapter 13, I explain how to download my repository which contains all updates of the Python scripts (codes) and simulations used in this Part 2 of the guide. Although I explain all the codes used in this guide clearly, if you need further help, just use my support link at the end of the Chapter. The truth is everyone needs help at one point or the other to learn and build automation in their development journey. I can give you more challenges and their solutions in my subsequent trainings.
Author: Bolakale Aremu
Publisher: AB Publisher LLC
ISBN:
Category : Computers
Languages : en
Pages : 46
Get Book Here
Book Description
This is part 1 of my 3-part training guide on how to build self-driving cars from scratch. This guide is bundled with a repository containing simulations, python scripts, graphics, and other useful assets. In this step-by-step guide, I’ll teach you how to make an app that you can use to create a simulation where cars learn how to drive autonomously over racing tracks. Here’s a break down of the contents of this guide. Part 1: Car mechanics. In this part, you’ll learn how to draw the car and control it with the keyboard. You will use a multimedia library for Python called Pyglet. This is the only library you will use in this guide. This is a cross-platform windowing and multimedia library for Python. It’s a powerful yet easy-to-use Python library for building games and other visually rich applications on Windows, macOS, and Linux. Part 2: Neural network and genetic algorithm. You’ll learn how to create the AI where you combine a neural network and genetic algorithm. You’ll learn how to add sensors to the car and get output from them. To prevent the untrained network from car crashes, a genetic algorithm will be used to train the cars. This will help the cars to drive simple tracks. Part 3: Challenges. You’ll add some challenges to the system. Tracks get more complicated and will take advantage from the previous track training by storing and retrieving the car brains. By the end of this training, you will have created self-driving cars that are capable of driving on unknown tracks by understanding how to steer, accelerate, and brake based on what cars see in front of them. Since autonomous cars need a brain of some kind, you know we need some AI (artificial intelligence). AI comes in many forms, but in this guide, you’ll use a neural network where the weights are adjusted by a genetic algorithm. Employment opportunities often come from work samples and concrete skills, rather than a college degree. So, you need to learn the practical aspect well enough. This guide will not only help you learn well and build a stunning portfolio, it will also provide you continuous help and support. With this book and my dedicated 24/7 help and support team, there’s nothing for you to fear. I have helped many Python developers update their automation development skills, launch successful careers and get hired for remote jobs. I notice that even the most ambitious beginners can run into problems, such as unable to decide where to begin. Sometimes they get completely lost on the way and therefore need further help. In Chapter 3, I explain how to download my repository which contains all updates of the Python scripts (codes) and simulations used in this guide. Although I explain all the codes used in this guide clearly, if you need further help, just use my support link at the end of the Chapter. The truth is everyone needs help at one point or the other to learn and build automation in their development journey. I can give you more challenges and their solutions in my subsequent trainings.
Author: Sumit Ranjan
Publisher: Packt Publishing Ltd
ISBN: 1838647023
Category : Computers
Languages : en
Pages : 320
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Book Description
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.
Author: Hod Lipson
Publisher: MIT Press
ISBN: 0262035227
Category : Architecture
Languages : en
Pages : 324
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Book Description
When human drivers let intelligent software take the wheel: the beginning of a new era in personal mobility.
Author: Pierluigi Coppola
Publisher: Elsevier
ISBN: 0128176962
Category : Transportation
Languages : en
Pages : 178
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Book Description
Autonomous Vehicles and Future Mobility presents novel methods for examining the long-term effects on individuals, society, and on the environment for a wide range of forthcoming transport scenarios, such as self-driving vehicles, workplace mobility plans, demand responsive transport analysis, mobility as a service, multi-source transport data provision, and door-to-door mobility. With the development and realization of new mobility options comes change in long-term travel behavior and transport policy. This book addresses these impacts, considering such key areas as the attitude of users towards new services, the consequences of introducing new mobility forms, the impacts of changing work related trips, and more. By examining and contextualizing innovative transport solutions in this rapidly evolving field, the book provides insights into the current implementation of these potentially sustainable solutions. It will serve as a resource of general guidelines and best practices for researchers, professionals and policymakers.
Author: Ilias Maglogiannis
Publisher: Springer Nature
ISBN: 3030491862
Category : Computers
Languages : en
Pages : 475
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Book Description
This 2 volume-set of IFIP AICT 583 and 584 constitutes the refereed proceedings of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, held in Neos Marmaras, Greece, in June 2020.* The 70 full papers and 5 short papers presented were carefully reviewed and selected from 149 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: Part I: classification; clustering - unsupervised learning -analytics; image processing; learning algorithms; neural network modeling; object tracking - object detection systems; ontologies - AI; and sentiment analysis - recommender systems. Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language. *The conference was held virtually due to the COVID-19 pandemic.
Author: Xin-Jun Liu
Publisher: Springer Nature
ISBN: 3030890988
Category : Computers
Languages : en
Pages : 853
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Book Description
The 4-volume set LNAI 13013 – 13016 constitutes the proceedings of the 14th International Conference on Intelligent Robotics and Applications, ICIRA 2021, which took place in Yantai, China, during October 22-25, 2021. The 299 papers included in these proceedings were carefully reviewed and selected from 386 submissions. They were organized in topical sections as follows: Robotics dexterous manipulation; sensors, actuators, and controllers for soft and hybrid robots; cable-driven parallel robot; human-centered wearable robotics; hybrid system modeling and human-machine interface; robot manipulation skills learning; micro_nano materials, devices, and systems for biomedical applications; actuating, sensing, control, and instrumentation for ultra-precision engineering; human-robot collaboration; robotic machining; medical robot; machine intelligence for human motion analytics; human-robot interaction for service robots; novel mechanisms, robots and applications; space robot and on-orbit service; neural learning enhanced motion planning and control for human robot interaction; medical engineering.
Author: David A. Mindell
Publisher: MIT Press
ISBN: 0262266687
Category : Technology & Engineering
Languages : en
Pages : 377
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Book Description
The incredible story of how human pilots and automated systems worked together to achieve the ultimate achievement in flight—the lunar landings of NASA’s Apollo program As Apollo 11’s Lunar Module descended toward the moon under automatic control, a program alarm in the guidance computer’s software nearly caused a mission abort. Neil Armstrong responded by switching off the automatic mode and taking direct control. He stopped monitoring the computer and began flying the spacecraft, relying on skill to land it and earning praise for a triumph of human over machine. In Digital Apollo, engineer-historian David Mindell takes this famous moment as a starting point for an exploration of the relationship between humans and computers in the Apollo program. In each of the six Apollo landings, the astronaut in command seized control from the computer and landed with his hand on the stick. Mindell recounts the story of astronauts’ desire to control their spacecraft in parallel with the history of the Apollo Guidance Computer. From the early days of aviation through the birth of spaceflight, test pilots and astronauts sought to be more than “spam in a can” despite the automatic controls, digital computers, and software developed by engineers. Digital Apollo examines the design and execution of each of the six Apollo moon landings, drawing on transcripts and data telemetry from the flights, astronaut interviews, and NASA’s extensive archives. Mindell’s exploration of how human pilots and automated systems worked together to achieve the ultimate in flight—a lunar landing—traces and reframes the debate over the future of humans and automation in space. The results have implications for any venture in which human roles seem threatened by automated systems, whether it is the work at our desktops or the future of exploration.
Author: Jason Torchinsky
Publisher:
ISBN: 9781948062978
Category : Philosophy
Languages : en
Pages :
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Book Description
From famed automotive journalist Jason Torchinsky, of Jalopnik and Jay Leno's Garage, comes a witty insider's guide to make sense of self-driving cars and predict the road ahead. Self-driving cars sound fantastical and futuristic and yet they'll soon be on every street in America. Whether it's Tesla's Autopilot, Google's Waymo, Mercedes's Distronic, or Uber's modified Volvos, companies around the world are developing autonomous cars. But why? And what will they mean for the auto industry and humanity at large? In Robot, Take the Wheel, Torchinsky gives a colorful account of the development of autonomous vehicles and their likely implications. He encourages us to think of self-driving cars as an entirely new machine, something beyond cars as we understand them today, and considers how humans will get along with these robots that will take over our cars' jobs, what they will look like, what sorts of jobs they may do, what we can expect of them, how they should act, ethically, how we can have fun with them, and how we can make sure there's still a place for those of us who love to drive, especially with a manual transmission. This vibrant volume explores what's ahead and what we can do now to shape the automated future.
Author: Markus Maurer
Publisher: Springer
ISBN: 3662488477
Category : Technology & Engineering
Languages : en
Pages : 698
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Book Description
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".
