Product Development within Artificial Intelligence, Ethics and Legal Risk, Winkle
Название: Artificial Intelligence of Things in Smart Environments ISBN: 3110755335 ISBN-13(EAN): 9783110755336 Издательство: Walter de Gruyter Рейтинг: Цена: 27884.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
In the De Gruyter Series on the Internet of Things, different approaches of the IoT, the challenges, and some of the applications are presented and discussed. This provides a roadmap to researchers and beginners.
IoT envisages a deep sense of connectivity and communication between the living and non-living entities. Today, the vision of IoT has expanded to connect everything - from industrial equipment, to everyday objects, to living organisms such as plants, farm animals, and people. To create a niche for non-living things to react, respond and work autonomously as and when required and as per their role, position and location in the ecosystem to provide services to the user. IoT is developing rapidly in industrial settings. IoT integrates the physical world with the information world so that every entity/device works for the betterment and in co-ordination with the other to help to conserve the most valued resources and save time.
Автор: Huseyin Abut, Kazuya Takeda, Gerhard Schmidt, John Hansen Название: Vehicles, Drivers, and Safety ISBN: 3110666472 ISBN-13(EAN): 9783110666472 Издательство: Walter de Gruyter Цена: 26024.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Автор: Haotian Cao, Xiaolin Song Название: Behavior Analysis and Modeling of Traffic Participants ISBN: 1636392644 ISBN-13(EAN): 9781636392646 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 14414.00 р. Наличие на складе: Нет в наличии.
Описание:
A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles.
However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition.
Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Описание: The variety of future powertrain concepts has drastically increased the development cost for automotive manufactures. Profitable investment requires a significantly leaner and efficient powertrain development process. Traditional methods of test and model based development need to be assisted by big data and data analytics. For this purpose, a valuable tool is available at the right time - artificial intelligence (AI). But what does AI really mean in a narrower sense? What concepts lie behind it? And how are the methods and algorithms transferable to powertrain applications? For the first time, this book aims to bridge the gap between automotive engineering and computer science, by illuminating the complexity of current AI concepts and demystifying it for powertrain applications. By elaborating on work processes, it shows how AI could be implemented and how completely novel methods can help us reshape the future of mobility.
Автор: Song Xiaolin, Cao Haotian Название: Behavior Analysis and Modeling of Traffic Participants ISBN: 1636392628 ISBN-13(EAN): 9781636392622 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11227.00 р. Наличие на складе: Нет в наличии.
Описание: To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology. Results and conclusions from this book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Автор: Brion van Over, Ute Winter, Elizabeth Molina-Markham, Sunny Lie, Donal Carbaugh Название: Communication in Vehicles: Cultural Variability in Speech Systems ISBN: 3110518910 ISBN-13(EAN): 9783110518917 Издательство: Walter de Gruyter Цена: 9288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
New technology in vehicles is transforming the way people move around as well as what they do in their vehicles. How does one communicate with an in-car speech system and how does this vary by language or cultural community? This book explores this process by focusing on the communication practices that people engage in when using their in-car systems and when talking about their vehicles with co-passengers. Chapters present a robust theory and methodology for studying communication in cars, how tasks are begun and ended, how people switch between tasks, how non-task talk appears, what ways and styles of communication drivers prefer, and how they expect the system voice to respond, among other things. Particular attention is given to cultural preferences as they are evident in this communication; these preferences are found to ground various trajectories in the use and meaning of in-car communication practices. The book explores these matters with a focus on the United States and Mainland China. Implications are drawn for the design and utilization of in-car communication systems.
Автор: Natasa Zivic, Obaid Ur-Rehman Название: Security in Autonomous Driving ISBN: 3110627078 ISBN-13(EAN): 9783110627077 Издательство: Walter de Gruyter Цена: 20446.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Autonomous driving is an emerging field. Vehicles are equipped with different systems such as radar, lidar, GPS etc. that enable the vehicle to make decisions and navigate without user's input, but there are still concerns regarding safety and security. This book analyses the security needs and solutions which are beneficial to autonomous driving.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Описание: 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.
Описание: The CAFE system has changed the way teachers assess, teach, and track student information, and positively impacted the way students learn. This updated, second edition demonstrates that reading instruction is not about the setting, the basal, or the book level. Rather, effective reading instruction is based on what the student needs in the moment.
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