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Author: Nico Schick Publisher: Cuvillier Verlag ISBN: 3736964536 Category : Computers Languages : en Pages : 30
Book Description
Approximately 3700 people die in traffic accidents each day. The most frequent cause of accidents is human error. Autonomous driving can significantly reduce the number of traffic accidents. To prepare autonomous vehicles for road traffic, the software and system components must be thoroughly validated and tested. However, due to their criticality, there is only a limited amount of data for safety-critical driving scenarios. Such driving scenarios can be represented in the form of time series. These represent the corresponding kinematic vehicle movements by including vectors of time, position coordinates, velocities, and accelerations. There are several ways to provide such data. For example, this can be done in the form of a kinematic model. Alternatively, methods of artificial intelligence or machine learning can be used. These are already being widely used in the development of autonomous vehicles. For example, generative algorithms can be used to generate safety-critical driving data. A novel taxonomy for the generation of time series and suitable generative algorithms will be described in this paper. In addition, a generative algorithm will be recommended and used to demonstrate the generation of time series associated with a typical example of a driving-critical scenario.
Author: Nico Schick Publisher: Cuvillier Verlag ISBN: 3736964536 Category : Computers Languages : en Pages : 30
Book Description
Approximately 3700 people die in traffic accidents each day. The most frequent cause of accidents is human error. Autonomous driving can significantly reduce the number of traffic accidents. To prepare autonomous vehicles for road traffic, the software and system components must be thoroughly validated and tested. However, due to their criticality, there is only a limited amount of data for safety-critical driving scenarios. Such driving scenarios can be represented in the form of time series. These represent the corresponding kinematic vehicle movements by including vectors of time, position coordinates, velocities, and accelerations. There are several ways to provide such data. For example, this can be done in the form of a kinematic model. Alternatively, methods of artificial intelligence or machine learning can be used. These are already being widely used in the development of autonomous vehicles. For example, generative algorithms can be used to generate safety-critical driving data. A novel taxonomy for the generation of time series and suitable generative algorithms will be described in this paper. In addition, a generative algorithm will be recommended and used to demonstrate the generation of time series associated with a typical example of a driving-critical scenario.
Author: Roberto Corlito Publisher: Cuvillier Verlag ISBN: 3736964544 Category : Computers Languages : en Pages : 20
Book Description
About 3700 people die in traffic accidents every day. Human error is the number one cause of accidents. Autonomous driving can greatly reduce the occurrence of traffic accidents. To release self-driving cars for road traffic, the system including software must be validated and tested efficiently. However, due to their criticality, the amount of data corresponding to safety-critical driving scenarios are limited. These driving scenes can be expressed as a time series. They represent the corresponding movement of the vehicle, including time vector, position coordinates, speed and acceleration. Such data can be provided on different ways. For example, in the form of a kinematic model. Alternatively, artificial intelligence or machine learning methods can be used. They have been widely used in the development of autonomous vehicles. For example, generative algorithms can be used to generate such safety-critical driving data. However, the validation of generative algorithms is a challenge in general. In most cases, their quality is assessed by means of expert knowledge (qualitative). In order to achieve a higher degree of automation, a quantitative validation approach is necessary. Generative algorithms are based on probability distributions or probability density functions. Accordingly, similarity measures can be used to evaluate generative algorithms. In this publication, such similarity measures are described and compared on the basis of defined evaluation criteria. With respect to the use case mentioned, a recommended similarity measure is implemented and validated for an example of a typical safety-critical driving scenario.
Author: Shaoshan Liu Publisher: Morgan & Claypool Publishers ISBN: 1681731673 Category : Computers Languages : en Pages : 285
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: Daniel Watzenig Publisher: Springer ISBN: 3319318950 Category : Technology & Engineering Languages : en Pages : 619
Book Description
The main topics of this book include advanced control, cognitive data processing, high performance computing, functional safety, and comprehensive validation. These topics are seen as technological bricks to drive forward automated driving. The current state of the art of automated vehicle research, development and innovation is given. The book also addresses industry-driven roadmaps for major new technology advances as well as collaborative European initiatives supporting the evolvement of automated driving. Various examples highlight the state of development of automated driving as well as the way forward. The book will be of interest to academics and researchers within engineering, graduate students, automotive engineers at OEMs and suppliers, ICT and software engineers, managers, and other decision-makers.
Author: Eitan Altman Publisher: Routledge ISBN: 1351458248 Category : Mathematics Languages : en Pages : 256
Book Description
This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction. The book is then divided into three sections that build upon each other.
Author: Markus Maurer Publisher: Springer ISBN: 3662488477 Category : Technology & Engineering Languages : en Pages : 698
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: Saurabh Mittal Publisher: John Wiley & Sons ISBN: 1119552397 Category : Technology & Engineering Languages : en Pages : 432
Book Description
Offers a one-stop reference on the application of advanced modeling and simulation (M&S) in cyber physical systems (CPS) engineering This book provides the state-of-the-art in methods and technologies that aim to elaborate on the modeling and simulation support to cyber physical systems (CPS) engineering across many sectors such as healthcare, smart grid, or smart home. It presents a compilation of simulation-based methods, technologies, and approaches that encourage the reader to incorporate simulation technologies in their CPS engineering endeavors, supporting management of complexity challenges in such endeavors. Complexity Challenges in Cyber Physical Systems: Using Modeling and Simulation (M&S) to Support Intelligence, Adaptation and Autonomy is laid out in four sections. The first section provides an overview of complexities associated with the application of M&S to CPS Engineering. It discusses M&S in the context of autonomous systems involvement within the North Atlantic Treaty Organization (NATO). The second section provides a more detailed description of the challenges in applying modeling to the operation, risk and design of holistic CPS. The third section delves in details of simulation support to CPS engineering followed by the engineering practices to incorporate the cyber element to build resilient CPS sociotechnical systems. Finally, the fourth section presents a research agenda for handling complexity in application of M&S for CPS engineering. In addition, this text: Introduces a unifying framework for hierarchical co-simulations of cyber physical systems (CPS) Provides understanding of the cycle of macro-level behavior dynamically arising from spaciotemporal interactions between parts at the micro-level Describes a simulation platform for characterizing resilience of CPS Complexity Challenges in Cyber Physical Systems has been written for researchers, practitioners, lecturers, and graduate students in computer engineering who want to learn all about M&S support to addressing complexity in CPS and its applications in today’s and tomorrow’s world.
Author: Gerson J. Alexander Publisher: ISBN: Category : Automobile driving Languages : en Pages : 48
Book Description
Expectancy relates to a driver's readiness to respond to situations, events, and information in predictable and successful ways. This report describes the concept of driver expectancy in the context of the driving task, and provides examples of expectancy and expectancy violations. It includes a procedure for identifying general and specific expectancy violations to enable engineers to develop remedial treatments to deal with expectancy problems.
Author: Hamid Jahankhani Publisher: Springer ISBN: 3319510649 Category : Computers Languages : en Pages : 414
Book Description
This book constitutes the refereed proceedings of the 11th International Conference on Global Security, Safety and Sustainability, ICGS3 2017, held in London, UK, in January, 2017. The 32 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers are organized in topical sections on the future of digital forensics; cyber intelligence and operation; information systems security management; systems security, safety, and sustainability; cyber infrastructure protection.
Author: Nico Schick
Author: Nico Schick
Author: Roberto Corlito
Author: Shaoshan Liu
Author: Daniel Watzenig
Author: Eitan Altman
Author: Markus Maurer
Author: Saurabh Mittal
Author: Christian Claudel
Author: Gerson J. Alexander
Author: Hamid Jahankhani