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Compiled from papers of the 4th Biennial Workshop on DSP (Digital Signal Processing) for In-Vehicle Systems and Safety this edited collection features world-class experts from diverse fields focusing on integrating smart in-vehicle systems with human factors to enhance safety in automobiles. Digital Signal Processing for In-Vehicle Systems and Safety presents new approaches on how to reduce driver inattention and prevent road accidents. The material addresses DSP technologies in adaptive automobiles, in-vehicle dialogue systems, human machine interfaces, video and audio processing, and in-vehicle speech systems. The volume also features recent advances in Smart-Car technology, coverage of autonomous vehicles that drive themselves, and information on multi-sensor fusion for driver ID and robust driver monitoring. Digital Signal Processing for In-Vehicle Systems and Safety is useful for engineering researchers, students, automotive manufacturers, government foundations and engineers working in the areas of control engineering, signal processing, audio-video processing, bio-mechanics, human factors and transportation engineering.
iHorizon-Enabled Energy Management for Electrified Vehicles proposes a realistic solution that assumes only scarce information is available prior to the start of a journey and that limited computational capability can be allocated for energy management. This type of framework exploits the available resources and closely emulates optimal results that are generated with an offline global optimal algorithm. In addition, the authors consider the present and future of the automotive industry and the move towards increasing levels of automation. Driver vehicle-infrastructure is integrated to address the high level of interdependence of hybrid powertrains and to comply with connected vehicle infrastructure. This book targets upper-division undergraduate students and graduate students interested in control applied to the automotive sector, including electrified powertrains, ADAS features, and vehicle automation. - Addresses the level of integration of electrified powertrains - Presents the state-of-the-art of electrified vehicle energy control - Offers a novel concept able to perform dynamic speed profile and energy demand prediction
World-class experts from academia and industry assembled at the sixth Biennial Workshop on Digital Signal Processing (DSP) for In-Vehicle Systems at Korea University, Seoul, Korea in 2013. The Workshop covered a wide spectrum of automotive fields, including in-vehicle signal processing and cutting-edge studies on safety, driver behavior, infrastructure, in-vehicle technologies. Contributors to this volume have expanded their contributions to the Workshop into full chapters with related works, methodology, experiments, and the analysis of the findings. Topics in this volume include: DSP technologies for in-vehicle systems Driver status and behavior monitoring In-Vehicle dialogue systems and human machine interfaces In-vehicle video and applications for safety Passive and active driver assistance technologies Ideas and systems for autonomous driving Transportation infrastructure
DSP for In-Vehicle and Mobile Systems is focused on digital signal processing strategies for improving information access, command and control, and communications for in-vehicle environments. It is expected that the next generation of human-to-vehicle interfaces will incorporate speech, video/image, and wireless communication modalities to provide more comfortable and safer driving ambiance. It is also expected that vehicles will become "smarter" and provide a level of wireless information sharing of resources regarding road, weather, traffic, and other information that drivers may need immediately or request at a later time while driving on the road. The format of this work centers on three themes: in-vehicle corpora, speech recognition/dialog systems with emphasis on car environments, and digital signal processing for mobile platforms involving noise suppression, image/video processing, and alternative communication scenarios that can be employed for in-vehicle applications. DSP for In-Vehicle and Mobile Systems is appropriate for researchers and professionals working in signal processing technologies, next generation vehicle design and networked-communications.
This is an edited collection by world-class experts, from diverse fields, focusing on integrating smart in-vehicle systems with human factors to enhance safety in automobiles. The book presents developments on road safety, in-vehicle technologies and state-of-the art systems. Includes coverage of DSP technologies in adaptive automobiles, algorithms and evaluation of in-car communication systems, driver-status monitoring and stress detection, in-vehicle dialogue systems and human-machine interfaces, challenges in video and audio processing for in-vehicle products, multi-sensor fusion for driver identification and vehicle to infrastructure wireless technologies.
Focusing on the vehicle's most important subsystems, this book features an introduction by the editor and 40 SAE technical papers from 2001-2006. The papers are organized in the following sections, which parallel the steps to be followed while building a complete final system: Introduction to Safety-Critical Automotive Systems Safety Process and Standards Requirements, Specifications, and Analysis Architectural and Design Methods and Techniques Prototyping and Target Implementation Testing, Verifications, and Validation Methods
In-Vehicle Corpus and Signal Processing for Driver Behavior is comprised of expanded papers from the third biennial DSPinCARS held in Istanbul in June 2007. The goal is to bring together scholars working on the latest techniques, standards, and emerging deployment on this central field of living at the age of wireless communications, smart vehicles, and human-machine-assisted safer and comfortable driving. Topics covered in this book include: improved vehicle safety; safe driver assistance systems; smart vehicles; wireless LAN-based vehicular location information processing; EEG emotion recognition systems; and new methods for predicting driving actions using driving signals. In-Vehicle Corpus and Signal Processing for Driver Behavior is appropriate for researchers, engineers, and professionals working in signal processing technologies, next generation vehicle design, and networks for mobile platforms.
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Loss prevention engineering describes all activities intended to help organizations in any industry to prevent loss, whether it be through injury, fire, explosion, toxic release, natural disaster, terrorism or other security threats. Compared to process safety, which only focusses on preventing loss in the process industry, this is a much broader field. Here is the only one-stop source for loss prevention principles, policies, practices, programs and methodology presented from an engineering vantage point. As such, this handbook discusses the engineering needs for manufacturing, construction, mining, defense, health care, transportation and quantification, covering the topics to a depth that allows for their functional use while providing additional references should more information be required. The reference nature of the book allows any engineers or other professionals in charge of safety concerns to find the information needed to complete their analysis, project, process, or design.
With a focus on cargo transportation, this book addresses the development of approaches intended to secure an infrastructure of smart services to support the adaptive implementation of online multi-modal freight transport management processes. It discusses the development of multi-criteria decision-making components and their integration into the multi-layered computer-based information management of intelligent systems. Through detailed descriptions of various components of intelligent transport management systems, the book demonstrates how to develop the services needed in the right place and at the right time, and how to properly adapt to user needs, making necessary interventions to ensure the safety of the transportation process. Further, it describes the main ways to increase the autonomy and efficiency of user-vehicle interaction and shows how Information and Communications Technology (ICT) structural support for current and past situations in AI-based systems can help to anticipate future developments in freight transportation.