Deep Learning For Autonomous Vehicle Control

Deep Learning For Autonomous Vehicle Control Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Deep Learning For Autonomous Vehicle Control book. This book definitely worth reading, it is an incredibly well-written.

Deep Learning for Autonomous Vehicle Control

Sampo Kuutti,Saber Fallah,Richard Bowden,Phil Barber
Deep Learning for Autonomous Vehicle Control Book PDF

Author : Sampo Kuutti,Saber Fallah,Richard Bowden,Phil Barber
Publisher : Springer Nature
Page : 70 pages
File Size : 41,9 Mb
Release : 2022-06-01
Category : Technology & Engineering
ISBN : 9783031015021

Get Book

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.

Deep Learning for Autonomous Vehicle Control

Sampo Kuutti,Amir Khajepour,Saber Fallah,Richard Bowden
Deep Learning for Autonomous Vehicle Control Book PDF

Author : Sampo Kuutti,Amir Khajepour,Saber Fallah,Richard Bowden
Publisher : Unknown
Page : 80 pages
File Size : 52,8 Mb
Release : 2019-08-08
Category : Electronic
ISBN : 1681736160

Get Book

Deep Learning for Autonomous Vehicle Control by Sampo Kuutti,Amir Khajepour,Saber Fallah,Richard Bowden 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.

Deep Learning for Autonomous Vehicle Control

Sampo Kuutti
Deep Learning for Autonomous Vehicle Control Book PDF

Author : Sampo Kuutti
Publisher : Unknown
Page : 82 pages
File Size : 44,8 Mb
Release : 2019
Category : Automobiles
ISBN : OCLC:1144222388

Get Book

Deep Learning for Autonomous Vehicle Control by Sampo Kuutti 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.

Applied Deep Learning and Computer Vision for Self-Driving Cars

Sumit Ranjan,Dr. S. Senthamilarasu
Applied Deep Learning and Computer Vision for Self Driving Cars Book PDF

Author : Sumit Ranjan,Dr. S. Senthamilarasu
Publisher : Packt Publishing Ltd
Page : 320 pages
File Size : 43,5 Mb
Release : 2020-08-14
Category : Computers
ISBN : 9781838647025

Get Book

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.

Autonomous Vehicles, Volume 1

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

Author : Romil Rawat,A. Mary Sowjanya,Syed Imran Patel,Varshali Jaiswal,Imran Khan,Allam Balaram
Publisher : John Wiley & Sons
Page : 324 pages
File Size : 49,8 Mb
Release : 2022-11-30
Category : Technology & Engineering
ISBN : 9781119871965

Get Book

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.

Artificial Intelligence for Autonomous Vehicles

Sathiyaraj Rajendran,Munish Sabharwal,Yu-Chen Hu,Rajesh Kumar Dhanaraj,Balamurugan Balusamy
Artificial Intelligence for Autonomous Vehicles Book PDF

Author : Sathiyaraj Rajendran,Munish Sabharwal,Yu-Chen Hu,Rajesh Kumar Dhanaraj,Balamurugan Balusamy
Publisher : John Wiley & Sons
Page : 208 pages
File Size : 44,6 Mb
Release : 2024-02-27
Category : Computers
ISBN : 9781119847632

Get Book

Artificial Intelligence for Autonomous Vehicles by Sathiyaraj Rajendran,Munish Sabharwal,Yu-Chen Hu,Rajesh Kumar Dhanaraj,Balamurugan Balusamy Pdf

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.

Deep Learning and Its Applications for Vehicle Networks

Fei Hu,Iftikhar Rasheed
Deep Learning and Its Applications for Vehicle Networks Book PDF

Author : Fei Hu,Iftikhar Rasheed
Publisher : CRC Press
Page : 608 pages
File Size : 53,8 Mb
Release : 2023-05-12
Category : Computers
ISBN : 9781000877250

Get Book

Deep Learning and Its Applications for Vehicle Networks by Fei Hu,Iftikhar Rasheed Pdf

Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.

Autonomous Vehicles and Systems

Ishwar K. Sethi
Autonomous Vehicles and Systems Book PDF

Author : Ishwar K. Sethi
Publisher : CRC Press
Page : 464 pages
File Size : 52,5 Mb
Release : 2024-02-06
Category : Technology & Engineering
ISBN : 9781003810674

Get Book

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

AI-enabled Technologies for Autonomous and Connected Vehicles

Yi Lu Murphey,Ilya Kolmanovsky,Paul Watta
AI enabled Technologies for Autonomous and Connected Vehicles Book PDF

Author : Yi Lu Murphey,Ilya Kolmanovsky,Paul Watta
Publisher : Springer Nature
Page : 563 pages
File Size : 52,9 Mb
Release : 2022-09-07
Category : Technology & Engineering
ISBN : 9783031067808

Get Book

AI-enabled Technologies for Autonomous and Connected Vehicles by Yi Lu Murphey,Ilya Kolmanovsky,Paul Watta Pdf

This book reports on cutting-edge research and advances in the field of intelligent vehicle systems. It presents a broad range of AI-enabled technologies, with a focus on automated, autonomous and connected vehicle systems. It covers advanced machine learning technologies, including deep and reinforcement learning algorithms, transfer learning and learning from big data, as well as control theory applied to mobility and vehicle systems. Furthermore, it reports on cutting-edge technologies for environmental perception and vehicle-to-everything (V2X), discussing socioeconomic and environmental implications, and aspects related to human factors and energy-efficiency alike, of automated mobility. Gathering chapters written by renowned researchers and professionals, this book offers a good balance of theoretical and practical knowledge. It provides researchers, practitioners and policy makers with a comprehensive and timely guide on the field of autonomous driving technologies.

Theories and Practices of Self-Driving Vehicles

Qingguo Zhou,Zebang Shen,Binbin Yong,Rui Zhao,Peng Zhi
Theories and Practices of Self Driving Vehicles Book PDF

Author : Qingguo Zhou,Zebang Shen,Binbin Yong,Rui Zhao,Peng Zhi
Publisher : Elsevier
Page : 346 pages
File Size : 40,7 Mb
Release : 2022-07-03
Category : Technology & Engineering
ISBN : 9780323994491

Get Book

Theories and Practices of Self-Driving Vehicles by Qingguo Zhou,Zebang Shen,Binbin Yong,Rui Zhao,Peng Zhi Pdf

Self-driving vehicles are a rapidly growing area of research and expertise. Theories and Practice of Self-Driving Vehicles presents a comprehensive introduction to the technology of self driving vehicles across the three domains of perception, planning and control. The title systematically introduces vehicle systems from principles to practice, including basic knowledge of ROS programming, machine and deep learning, as well as basic modules such as environmental perception and sensor fusion. The book introduces advanced control algorithms as well as important areas of new research. This title offers engineers, technicians and students an accessible handbook to the entire stack of technology in a self-driving vehicle. Theories and Practice of Self-Driving Vehicles presents an introduction to self-driving vehicle technology from principles to practice. Ten chapters cover the full stack of driverless technology for a self-driving vehicle. Written by two authors experienced in both industry and research, this book offers an accessible and systematic introduction to self-driving vehicle technology. Provides a comprehensive introduction to the technology stack of a self-driving vehicle Covers the three domains of perception, planning and control Offers foundational theory and best practices Introduces advanced control algorithms and high-potential areas of new research Gives engineers, technicians and students an accessible handbook to self-driving vehicle technology and applications

Deep Learning for Unmanned Systems

Anis Koubaa,Ahmad Taher Azar
Deep Learning for Unmanned Systems Book PDF

Author : Anis Koubaa,Ahmad Taher Azar
Publisher : Springer Nature
Page : 731 pages
File Size : 46,6 Mb
Release : 2021-10-01
Category : Technology & Engineering
ISBN : 9783030779399

Get Book

Deep Learning for Unmanned Systems by Anis Koubaa,Ahmad Taher Azar Pdf

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.

Creating Autonomous Vehicle Systems

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

Author : Liu Shaoshan,Li Liyun,Tang Jie,Wu Shuang,Gaudiot Jean-Luc
Publisher : Springer Nature
Page : 192 pages
File Size : 47,9 Mb
Release : 2017-10-25
Category : Mathematics
ISBN : 9783031018022

Get Book

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.

Learning to Drive

David Michael Stavens
Learning to Drive Book PDF

Author : David Michael Stavens
Publisher : Stanford University
Page : 104 pages
File Size : 43,7 Mb
Release : 2011
Category : Electronic
ISBN : STANFORD:pb661px9942

Get Book

Learning to Drive by David Michael Stavens Pdf

Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.

Human-Like Decision Making and Control for Autonomous Driving

Peng Hang,Chen Lv,Xinbo Chen
Human Like Decision Making and Control for Autonomous Driving Book PDF

Author : Peng Hang,Chen Lv,Xinbo Chen
Publisher : CRC Press
Page : 201 pages
File Size : 45,9 Mb
Release : 2022-07-25
Category : Mathematics
ISBN : 9781000624953

Get Book

Human-Like Decision Making and Control for Autonomous Driving by Peng Hang,Chen Lv,Xinbo Chen Pdf

This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.

Smart Transportation

Guido Dartmann,Anke Schmeink,Volker Lücken,Houbing Song,Martina Ziefle,Giovanni Prestiflippo
Smart Transportation Book PDF

Author : Guido Dartmann,Anke Schmeink,Volker Lücken,Houbing Song,Martina Ziefle,Giovanni Prestiflippo
Publisher : CRC Press
Page : 256 pages
File Size : 44,6 Mb
Release : 2021-11-10
Category : Technology & Engineering
ISBN : 9781000405637

Get Book

Smart Transportation by Guido Dartmann,Anke Schmeink,Volker Lücken,Houbing Song,Martina Ziefle,Giovanni Prestiflippo Pdf

The book provides a broad overview of the challenges and recent developments in the field of smart mobility and transportation, including technical, algorithmic and social aspects of smart mobility and transportation. It reviews new ideas for services and platforms for future mobility. New concepts of artificial intelligence and the implementation in new hardware architecture are discussed. In the context of artificial intelligence, new challenges of machine learning for autonomous vehicles and fleets are investigated. The book also investigates human factors and social questions of future mobility concepts. The goal of this book is to provide a holistic approach towards smart transportation. The book reviews new technologies such as the cloud, machine learning and communication for fully atomatized transport, catering to the needs of citizens. This will lead to complete change of concepts in transportion.