Digital Signal Processing For In Vehicle Systems And Safety

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.

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

This book presents works from world-class experts from academia, industry, and national agencies representing countries from across the world focused on automotive fields for in-vehicle signal processing and safety. These include cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces. Vehicle Systems, Driver Modeling and Safety is appropriate for researchers, engineers, and professionals working in signal processing for vehicle systems, next generation system design from driver-assisted through fully autonomous vehicles.

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.

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

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.

A large number of heavy plant vehicles are utilised within the quarrying, mining and construction industries in order to facilitate the type of operations inherent to this type of industry. The use of such vehicles unavoidably results in safety hazards which, in some cases, can be reduced by use of contemporary electronic techniques. A development programme was completed over a two year period to expand and enhance the range of electronic safety aids for heavy plant vehicles manufactured by Ogden Safety Systems Limited. The work comprised the re-design of the signal processing element of a current product, an anti-collision microwave radar, for use during vehicle reversing and the design and development of a new product, intended to be a low cost method of brake testing such vehicles. Both products made use of digital signal processing techniques based on a 16-bit single-chip microprocessor with algorithms coded in the high level language C. The design of both products made extensive use of common hardware and software building blocks in order to reduce development time. The radar system used a varactor tuned Gunn oscillator microwave device as the detection element and the brake testing device utilised a micro-machined silicon accelerometer as the sensing element. Suitable analogue signal processing circuitry was designed for each product to condition the analogue signal from the relevant sensing element into a form suitable for digital signal processing.