Author: Hamid Tahir
Publisher:
ISBN:
Category : Automated guided vehicle systems
Languages : en
Pages : 97
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Book Description
Autonomous vehicles and their related development are gaining a lot of traction as a promising up and coming technology. The Mechatronics Vehicle Systems lab at the University of Waterloo is well pioneered in the automotive industry and seeks to apply their knowledge and skills to autonomous vehicles. Having an autonomous vehicle development platform at the University allows for development and testing of state of the art algorithms that can potentially benefit the entire automotive industry. An autonomous driving platform based on a Chevrolet Equinox is proposed in this thesis. Various types of sensors are installed on the vehicle and interfaced, allowing for full coverage of the surrounding environment. A software platform is developed which uses ROS and Matlab simultaneously, benefiting from the libraries, tools, and resources that come with both. The hardware platform is designed with simplicity and functionality in mind. Moreover, a simulation platform is used for testing various algorithms before real world implementation. Various types of sensor calibrations are necessary to fully synchronize all the sensors on the platform spatially. A joint calibration method that allows for the simultaneous calibration of all 3D sensors sharing a common field of view is implemented. Specialized hand-eye calibration methods to calibrate the GPS navigation system to the LIDAR and camera sensors are explored. Furthermore, vehicle to everything interfacing is kept in mind and a calibration technique is presented in order to localize infrastructure mounted sensors to a GPS navigation system. The calibration techniques are tested and areas of improvement are revealed. The developed platform is tested with the task of autonomous lane keeping. The steering wheel angle of the vehicle is controlled by the developed algorithm utilizing the camera and GPS navigation solution. The algorithm is tested in simulation with good results. Before real world testing, time synchronization between various devices on the platform, as well as testing of the actuators' controllers is performed. Finally, the lane keeping algorithm is tested on the developed platform on the University of Waterloo Ring Road. The system is able to autonomously steer around the majority of the road which is approximately a 2.5 km distance.
Author: Shaoshan Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681731673
Category : Computers
Languages : en
Pages : 285
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Book Description
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: James M. Anderson
Publisher: Rand Corporation
ISBN: 0833084372
Category : Transportation
Languages : en
Pages : 215
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Book Description
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
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".
Author: Liu Shaoshan
Publisher: Springer Nature
ISBN: 3031018052
Category : Mathematics
Languages : en
Pages : 221
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Book Description
This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing 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 as to its future 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, new algorithms can be tested so as to update the HD map—in addition to training better recognition, tracking, and decision models. Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled “Teaching and Learning from this Book” was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects. 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 extensive references for an effective, deeper exploration of the various technologies.
Author: Sam Hind
Publisher: Springer Nature
ISBN: 9819717493
Category :
Languages : en
Pages : 278
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Book Description
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 on a wide range of forthcoming transport scenarios such 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. Autonomous Vehicles and Future Mobility addresses these impacts, considering such key areas as attitude of users towards new services, the consequences of introducing of new mobility forms, the impacts of changing work related trips, the access to information about mobility options and the changing strategies of relevant stakeholders in transportation. By examining and contextualizing innovative transport solutions in this rapidly evolving field, Autonomous Vehicles and Future Mobility provides insights into current implementation of these potentially sustainable solutions, serving as general guidelines and best practices for researchers, professionals, and policy makers. Covers hot topics including travel behavior change, autonomous vehicle impacts, intelligent solutions, mobility planning, mobility as a service, sustainable solutions, and more Examines up to date models and applications using novel technologies Contributions from leading scholars around the globe Case studies with latest research results
Author: Shaoshan Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681730081
Category : Computers
Languages : en
Pages : 198
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Book Description
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: George Dekoulis
Publisher: BoD – Books on Demand
ISBN: 1839629215
Category : Transportation
Languages : en
Pages : 212
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Book Description
This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field.
Author: Kamal Malik
Publisher: CRC Press
ISBN: 1040099297
Category : Computers
Languages : en
Pages : 205
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Book Description
Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance. This book: Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems. Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles. Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making. Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control. Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles. The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.
