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Simulated Self Driving Car Using Deep Learning
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Book Synopsis Simulated Self-driving Car Using Deep Learning by : Vinayak Jayajee Jethe
Download or read book Simulated Self-driving Car Using Deep Learning written by Vinayak Jayajee Jethe and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles have the potential to change the world. Having a self-driving car will not only help save time of drivers and allow them to relax, but also help reduce the number of accidents caused by carelessness of drivers. These autonomous systems are built over highly complex layers of neural networks which are responsible for producing the necessary results. In order to test these self-driving cars, directly using them in the real world could be a really big risk factor. That is the reason, using a simulator can help overcome this problem by allowing us to test our convolutional neural network in a simulated environment. It will bring the power to constantly improve the efficiency of our network. There are various simulators which are available for research and development purposes, such as Carla, AirSim by Mircrosoft, and many more. The main purpose of these simulators is to try and test various neural networks that on further enhancements could possibly change the way we drive. This project focuses on simulated self-driving car using deep learning techniques. The goal is to drive a simulated car in autonomous mode without any human interaction based on how well the model is trained. It uses the udacity open-source self-driving car simulator, which is built for the purpose of learning self-driving vehicle technology and the convolutional neural network.
Book Synopsis Applied Deep Learning and Computer Vision for Self-Driving Cars by : Sumit Ranjan
Download or read book Applied Deep Learning and Computer Vision for Self-Driving Cars written by Sumit Ranjan and published by Packt Publishing Ltd. This book was released on 2020-08-14 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) by : IEEE Staff
Download or read book 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) written by IEEE Staff and published by . This book was released on 2020-12-03 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In today s world, Sustainable development is becoming a crucial part to meet the increasing demand of future generations The 3rd International Conference on Intelligent Sustainable Systems ICISS 2020 is one of the initiative toward attaining sustainable development and facilitating collaborative forums in international level This conference provides unique opportunity to bring together academicians, researchers, scientists and research scholars to share and exchange ideas and practical solutions towards achievement of intelligent sustainable systems for a more sustainable future This conference also aims to create an interdisciplinary platform to share their research ideas on developing new models and algorithms for sustainable development and provide intelligent paradigm shifts to deal with uncertainties and imprecise problems in real world
Book Synopsis Self-Driving Car Simulation using Adaboost-CNN Algorithm by : Ali Mohammad Tarif
Download or read book Self-Driving Car Simulation using Adaboost-CNN Algorithm written by Ali Mohammad Tarif and published by GRIN Verlag. This book was released on 2018-01-15 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2017 in the subject Engineering - Automotive Engineering, grade: 2.00, International Islamic University Malaysia, course: CSC 3304: Machine Learning, language: English, abstract: People spend hours to drive their car from place to place. What if a person sets its destination and goes to sleep while the car drives itself to the destination? It will save plenty of time. Tesla already started selling autopilot cars. Though the car can drive itself but is trustable only in certain quality roads. This means, research should still be carried out in self driving car project. All of the existing self-driving car simulation projects used Convolutional Neural Network as learning method. Though Adaboost is mostly used with binary classification problem, a variant can be developed to adapt Adaboost with Convolutional Neural Network.
Book Synopsis The Complete Self-Driving Car Course - Applied Deep Learning by : Rayan Slim
Download or read book The Complete Self-Driving Car Course - Applied Deep Learning written by Rayan Slim and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python About This Video The transition from a beginner to deep learning expert Learn through demonstrations as your instructor completes each task with you No experience required In Detail Self-driving cars have emerged to be one of the most transformative technologies. Fueled by deep learning algorithms, they are rapidly developing and creating new opportunities in the mobility sector. Deep learning jobs command some of the highest salaries in the development world. This is the first and one of the only courses that make practical use of deep learning and applies it to building a self-driving car. You'll learn and master deep learning in this fun and exciting course with top instructor Rayan Slim. Having trained thousands of students, Rayan is a highly rated and experienced instructor who follows a learning-by-doing approach. By the end of the course, you will have built a fully functional self-driving car powered entirely by deep learning. This powerful simulation will impress even the most senior developers and ensure you have hands-on skills in neural networks that you can bring to any project or company. This course will show you how to do the following: Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car Train a perceptron-based neural network to classify between binary classes Train convolutional neural networks to identify various traffic signs Train deep neural networks to fit complex datasets Master Keras, a power neural network library written in Python Build and train a fully functional self-driving car Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course--Applied-Deep-Learning . If you require support please email: [email protected].
Book Synopsis Deep Learning for Autonomous Vehicle Control by : Sampo Kuutti
Download or read book Deep Learning for Autonomous Vehicle Control written by Sampo Kuutti and published by Morgan & Claypool Publishers. This book was released on 2019-08-08 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
Book Synopsis Emerging Technologies in Data Mining and Information Security by : João Manuel R. S. Tavares
Download or read book Emerging Technologies in Data Mining and Information Security written by João Manuel R. S. Tavares and published by Springer Nature. This book was released on 2021-05-04 with total page 994 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of things (IoT), and information security.
Book Synopsis 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) by : IEEE Staff
Download or read book 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) written by IEEE Staff and published by . This book was released on 2021-03-05 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference aims to present a unified platform for advanced and multi disciplinary research towards design of smart computing and informatics The theme is on a broader front focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries The scope is also extended towards deployment of emerging computational and knowledge transfer approaches, optimizing solutions in varied disciplines of science, technology and healthcare
Book Synopsis Practical Simulations for Machine Learning by : Paris Buttfield-Addison
Download or read book Practical Simulations for Machine Learning written by Paris Buttfield-Addison and published by "O'Reilly Media, Inc.". This book was released on 2022-06-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That’s just the beginning. With this practical book, you’ll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits
Book Synopsis 2018 15th International Conference on Ubiquitous Robots (UR) by :
Download or read book 2018 15th International Conference on Ubiquitous Robots (UR) written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Creating Autonomous Vehicle Systems by : Shaoshan Liu
Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.
Book Synopsis Intelligent Computing, Information and Control Systems by : A. Pasumpon Pandian
Download or read book Intelligent Computing, Information and Control Systems written by A. Pasumpon Pandian and published by Springer Nature. This book was released on 2019-10-18 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: From past decades, Computational intelligence embraces a number of nature-inspired computational techniques which mainly encompasses fuzzy sets, genetic algorithms, artificial neural networks and hybrid neuro-fuzzy systems to address the computational complexities such as uncertainties, vagueness and stochastic nature of various computational problems practically. At the same time, Intelligent Control systems are emerging as an innovative methodology which is inspired by various computational intelligence process to promote a control over the systems without the use of any mathematical models. To address the effective use of intelligent control in Computational intelligence systems, International Conference on Intelligent Computing, Information and Control Systems (ICICCS 2019) is initiated to encompass the various research works that helps to develop and advance the next-generation intelligent computing and control systems. This book integrates the computational intelligence and intelligent control systems to provide a powerful methodology for a wide range of data analytics issues in industries and societal applications. The recent research advances in computational intelligence and control systems are addressed, which provide very promising results in various industry, business and societal studies. This book also presents the new algorithms and methodologies for promoting advances in common intelligent computing and control methodologies including evolutionary computation, artificial life, virtual infrastructures, fuzzy logic, artificial immune systems, neural networks and various neuro-hybrid methodologies. This book will be pragmatic for researchers, academicians and students dealing with mathematically intransigent problems. It is intended for both academicians and researchers in the field of Intelligent Computing, Information and Control Systems, along with the distinctive readers in the fields of computational and artificial intelligence to gain more knowledge on Intelligent computing and control systems and their real-world applications.
Book Synopsis Machine Learning for Predictive Analysis by : Amit Joshi
Download or read book Machine Learning for Predictive Analysis written by Amit Joshi and published by Springer Nature. This book was released on 2020-10-22 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains, machine learning, intelligent information retrieval and business intelligence, intelligent information system development using design science principles, intelligent web mining and knowledge discovery systems.
Book Synopsis Artificial Intelligence for Autonomous Vehicles by : Sathiyaraj Rajendran
Download or read book Artificial Intelligence for Autonomous Vehicles written by Sathiyaraj Rajendran and published by John Wiley & Sons. This book was released on 2024-02-27 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of advanced technologies in AI, driverless vehicles have elevated curiosity among various sectors of society. The automotive industry is in a technological boom with autonomous vehicle concepts. Autonomous driving is one of the crucial application areas of Artificial Intelligence (AI). Autonomous vehicles are armed with sensors, radars, and cameras. This made driverless technology possible in many parts of the world. In short, our traditional vehicle driving may swing to driverless technology. Many researchers are trying to come out with novel AI algorithms that are capable of handling driverless technology. The current existing algorithms are not able to support and elevate the concept of autonomous vehicles. This addresses the necessity of novel methods and tools focused to design and develop frameworks for autonomous vehicles. There is a great demand for energy-efficient solutions for managing the data collected with the help of sensors. These operations are exclusively focused on non-traditional programming approaches and depend on machine learning techniques, which are part of AI. There are multiple issues that AI needs to resolve for us to achieve a reliable and safe driverless technology. The purpose of this book is to find effective solutions to make autonomous vehicles a reality, presenting their challenges and endeavors. The major contribution of this book is to provide a bundle of AI solutions for driverless technology that can offer a safe, clean, and more convenient riskless mode of transportation.
Book Synopsis Person Re-Identification by : Shaogang Gong
Download or read book Person Re-Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.
Book Synopsis How Self-Driving Cars Work by : Ian Chow-Miller
Download or read book How Self-Driving Cars Work written by Ian Chow-Miller and published by Cavendish Square Publishing, LLC. This book was released on 2018-07-15 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Once considered a possibility of the distant future, the technology for self-driving vehicles may soon be fully realized and widely available. In this timely resource, young readers will discover how self-driving cars work, how they move safely about the road, and how these amazing innovations have evolved from the automobile as we know it.
Book Synopsis Autonomous driving algorithms and Its IC Design by : Jianfeng Ren
Download or read book Autonomous driving algorithms and Its IC Design written by Jianfeng Ren and published by Springer Nature. This book was released on 2023-08-09 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.
