Deep Neural Networks And Data For Automated Driving

Author : Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben
Publisher : Springer Nature
Page : 435 pages
File Size : 40,6 Mb
Release : 2022-07-19
Category : Technology & Engineering
ISBN : 9783031012334

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Deep Neural Networks and Data for Automated Driving by Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Pdf

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Author : Sumit Ranjan,Dr. S. Senthamilarasu
Publisher : Packt Publishing Ltd
Page : 320 pages
File Size : 54,9 Mb
Release : 2020-08-14
Category : Computers
ISBN : 9781838647025

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Applied Deep Learning and Computer Vision for Self-Driving Cars by Sumit Ranjan,Dr. S. Senthamilarasu Pdf

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 : Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben
Publisher : Unknown
Page : 0 pages
File Size : 45,5 Mb
Release : 2022
Category : Electronic
ISBN : 3031034899

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Deep Neural Networks and Data for Automated Driving by Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Pdf

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Author : Sampo Kuutti,Saber Fallah,Richard Bowden,Phil Barber
Publisher : Morgan & Claypool Publishers
Page : 82 pages
File Size : 47,6 Mb
Release : 2019-08-08
Category : Technology & Engineering
ISBN : 9781681736082

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Deep Learning for Autonomous Vehicle Control by Sampo Kuutti,Saber Fallah,Richard Bowden,Phil Barber Pdf

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.

Author : Sumit Ranjan,S. Senthamilarasu
Publisher : Unknown
Page : 332 pages
File Size : 48,7 Mb
Release : 2020-08-14
Category : Computers
ISBN : 1838646302

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Applied Deep Learning and Computer Vision for Self-Driving Cars by Sumit Ranjan,S. Senthamilarasu Pdf

Author : Soham Sarkar,Abhishek Basu,Siddhartha Bhattacharyya
Publisher : John Wiley & Sons
Page : 292 pages
File Size : 45,5 Mb
Release : 2021-04-05
Category : Technology & Engineering
ISBN : 9781119571384

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Intelligent Multi-Modal Data Processing by Soham Sarkar,Abhishek Basu,Siddhartha Bhattacharyya Pdf

A comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors – noted experts on the topic – offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: Includes an in-depth analysis of the state-of-the-art applications of signal and data processing Contains contributions from noted experts in the field Offers information on hybrid differential evolution for optimal multilevel image thresholding Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, Intelligent Multi-Modal Data Processing brings together in one volume the range of multi-modal data processing.

Author : Jianfeng Ren,Dong Xia
Publisher : Springer Nature
Page : 306 pages
File Size : 54,8 Mb
Release : 2023-08-09
Category : Technology & Engineering
ISBN : 9789819928972

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Autonomous driving algorithms and Its IC Design by Jianfeng Ren,Dong Xia Pdf

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.

Author : Khaled R. Ahmed,Aboul Ella Hassanien
Publisher : Springer Nature
Page : 264 pages
File Size : 45,5 Mb
Release : 2021-04-10
Category : Computers
ISBN : 9783030656614

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Deep Learning and Big Data for Intelligent Transportation by Khaled R. Ahmed,Aboul Ella Hassanien Pdf

This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today’s technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle’s speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.

Author : Ishwar K. Sethi
Publisher : CRC Press
Page : 464 pages
File Size : 46,9 Mb
Release : 2024-02-06
Category : Technology & Engineering
ISBN : 9781003810674

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Autonomous Vehicles and Systems by Ishwar K. Sethi Pdf

This book captures multidisciplinary research encompassing various facets of autonomous vehicle systems (AVS) research and developments. The AVS field is rapidly moving towards realization with numerous advances continually reported. The contributions to this field come from widely varying branches of knowledge, making it a truly multidisciplinary area of research and development. The topics covered in the book include: AI and deep learning for AVS Autonomous steering through deep neural networks Adversarial attacks and defenses on autonomous vehicles Gesture recognition for vehicle control Multi-sensor fusion in autonomous vehicles Teleoperation technologies for AVS Simulation and game theoretic decision making for AVS Path following control system design for AVS Hybrid cloud and edge solutions for AVS Ethics of AVS

Author : Romil Rawat,A. Mary Sowjanya,Syed Imran Patel,Varshali Jaiswal,Imran Khan,Allam Balaram
Publisher : John Wiley & Sons
Page : 324 pages
File Size : 54,9 Mb
Release : 2022-11-30
Category : Technology & Engineering
ISBN : 9781119871965

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Autonomous Vehicles, Volume 1 by Romil Rawat,A. Mary Sowjanya,Syed Imran Patel,Varshali Jaiswal,Imran Khan,Allam Balaram Pdf

AUTONOMOUS VEHICLES Addressing the current challenges, approaches and applications relating to autonomous vehicles, this groundbreaking new volume presents the research and techniques in this growing area, using Internet of Things (IoT), Machine Learning (ML), Deep Learning, and Artificial Intelligence (AI). This book provides and addresses the current challenges, approaches, and applications relating to autonomous vehicles, using Internet of Things (IoT), machine learning, deep learning, and Artificial Intelligence (AI) techniques. Several self-driving or autonomous (“driverless”) cars, trucks, and drones incorporate a variety of IoT devices and sensing technologies such as sensors, gyroscopes, cloud computing, and fog layer, allowing the vehicles to sense, process, and maintain massive amounts of data on traffic, routes, suitable times to travel, potholes, sharp turns, and robots for pipe inspection in the construction and mining industries. Few books are available on the practical applications of unmanned aerial vehicles (UAVs) and autonomous vehicles from a multidisciplinary approach. Further, the available books only cover a few applications and designs in a very limited scope. This new, groundbreaking volume covers real-life applications, business modeling, issues, and solutions that the engineer or industry professional faces every day that can be transformed using intelligent systems design of autonomous systems. Whether for the student, veteran engineer, or another industry professional, this book, and its companion volume, are must-haves for any library.

Author : Liviu A. MARINA,Florin D. MOLDOVEANU,Sorin M. GRIGORESCU
Publisher : Infinite Study
Page : 20 pages
File Size : 44,8 Mb
Release : 2024-06-03
Category : Mathematics
ISBN : 8210379456XXX

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Real-time Driving Context Understanding using Deep Grid Net: A Granular Approach by Liviu A. MARINA,Florin D. MOLDOVEANU,Sorin M. GRIGORESCU Pdf

Numerous self-driving cars algorithms rely on grid maps for motion planning, obstacles avoidance or environment perception. Obtained from fused sensory information, the occupancy grids (OGs) are nowadays among the most popular solutions used in series production in automotive industry. In this paper, we extend Deep Grid Net (DGN), a deep learning(DL) system designed for understanding the context in which an autonomous car is driving.We consider this paper a granular approach to DGN method due to the improvements added to the original research.

Author : Khaled Salah Mohamed
Publisher : Springer Nature
Page : 216 pages
File Size : 53,6 Mb
Release : 2023-06-23
Category : Technology & Engineering
ISBN : 9783031357374

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Deep Learning-Powered Technologies by Khaled Salah Mohamed Pdf

This book covers various, leading-edge deep learning technologies. The author discusses new applications of deep learning and gives insight into the integration of deep learning with various application domains, such as autonomous driving, augmented reality, AIOT, 5G and beyond.

Author : Liu Shaoshan,Li Liyun,Tang Jie,Wu Shuang,Gaudiot Jean-Luc
Publisher : Springer Nature
Page : 192 pages
File Size : 50,5 Mb
Release : 2017-10-25
Category : Mathematics
ISBN : 9783031018022

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Creating Autonomous Vehicle Systems by Liu Shaoshan,Li Liyun,Tang Jie,Wu Shuang,Gaudiot Jean-Luc Pdf

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.

Author : Hod Lipson,Melba Kurman
Publisher : MIT Press
Page : 323 pages
File Size : 51,6 Mb
Release : 2017-09-15
Category : Transportation
ISBN : 9780262534475

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Driverless by Hod Lipson,Melba Kurman Pdf

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.”

Author : Frank Kane
Publisher : Unknown
Page : 128 pages
File Size : 41,8 Mb
Release : 2019
Category : Electronic
ISBN : 1838988467

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Autonomous Cars by Frank Kane Pdf

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars. About This Video Learn complex topics such as artificial intelligence (AI) and machine learning through a systematic and helpful teaching style Build deep neural networks with TensorFlow and Keras Classify data with machine learning techniques such as regression, decision trees, Naive Bayes, and SVM In Detail The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles to self-driving, artificial intelligence-powered vehicles. As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial. This course will guide you through the key design and development aspects of self-driving vehicles. You'll be exploring OpenCV, deep learning, and artificial neural networks and their role in the development of autonomous cars. The book will even guide you through classifying traffic signs with convolutional neural networks (CNNs). In addition to this, you'll use template matching to identify other vehicles in images, along with understanding how to apply HOG for extracting image features. As you progress, you'll gain insights into feature detectors, including SIFT, SURF, FAST, and ORB. Next, you'll get up to speed with building neural networks using Keras and TensorFlow, and later focus on linear regression and logistic regression. Toward the concluding part, you'll explore machine learning techniques such as decision trees and Naive Bayes for classifying data, in addition to understanding the Support Vector Machine (SVM) method. By the end of this course, you'll be well-versed with key concepts related to the design and development of self-driving vehicles. 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/Autonomous-Cars-Deep-Learning-and-Computer-Vision-in-Python . If you require support please email: [email protected].