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Author: Jiadi Yu Publisher: Springer ISBN: 3319897705 Category : Computers Languages : en Pages : 81
Book Description
This SpringerBrief begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones. As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented. However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost. The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors. Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.
Author: Jiadi Yu Publisher: Springer ISBN: 3319897705 Category : Computers Languages : en Pages : 81
Book Description
This SpringerBrief begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones. As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented. However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost. The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors. Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.
Author: Landu Jiang Publisher: ISBN: Category : Languages : en Pages :
Book Description
"Drivers' "illegal maneuver" and "unsafe behavior" contribute to a large number of traffic accidents every year, which are now receiving great attention from both government regulators and car manufacturers. Indeed, many research efforts have been dedicated to understanding and recognizing dangerous driving conditions to prevent crashes and injuries. In addition to the features that are already installed in the vehicles, enhancing driving safety via mobile sensing techniques (e.g., smartphones and wearables) is becoming increasingly successful with the deep penetration of smart computing. The mobile device today is equipped with numerous sensors, which has become a very effective platform to facilitate various safety applications. In this thesis, we leverage off-the-shelf mobile sensing platforms (i.e., smartphones and wrist-worn devices) to detect and analyze dangerous driving events. Our purpose is to use real-time alerts and long-term feedbacks to increase drivers' awareness of dangerous behaviors, which could help them shape good driving habits and promote safety. Specifically, two studies are presented: 1. SafeCam - analyzing intersection-related driver behaviors using smartphone sensors, and 2. SafeDrive - monitoring distracted driving behaviors using wrist-worn devices (e.g., smartwatch). The first study focuses on the intersection safety which is a critical issue in current roadway systems. In the United States, nearly one-quarter of traffic fatalities and half of all traffic injuries are attributed to intersections. We design SafeCam that uses embedded sensors (i.e., inertial sensors and cameras) on the smartphone to track vehicle dynamics while at the same time adopts computer vision algorithms to recognize traffic control information (e.g., traffic lights and stop signs). The system is able to detect dangerous driving events not only on roads but also at intersections including speeding, lane waving, unsafe turns, running stop signs and running red lights. Our second study addresses the distracted driving problem that has been considered as a major threat to the traffic safety. It is estimated that roughly 30% of vehicle fatalities involve distracted drivers, which cause thousands of injuries and deaths every year in the United States. SafeDrive is a driving safety system that leverages the wrist-worn (i.e.,smartwatch) sensors to prevent driver distractions. By tracking driver's hand motion and utilizing machine learning algorithms, SafeDrive can detect five most common distracting activities including fiddling with the control (e.g., infotainment systems), drinking/eating, using smartphones, searching items at the passenger side and reaching back seats. In the evaluation, we conduct extensive real-road experiments using different types of vehicles (e.g., sedan, minivan, and SUV) and recruiting multiple participants (15 for SafeCam and 20 for SafeDrive). The experiment results demonstrate that both SafeCam and SafeDrive are robust to real-driving environments, which could detect critical driving events and have great potential to educate drivers on how to safely operate the vehicle." --
Author: Nanxiang Li Publisher: ISBN: Category : Automobile driving Languages : en Pages : 262
Book Description
With the development of new in-vehicle technology, drivers are exposed to more sources of distractions, which can lead to unintentional accidents. Monitoring the driver attention level has become a relevant research problem. Many studies aim to understand the driver behavior using measurements from different perspectives, such as driving task performance, secondary task performance and driver physiology signal. Although these studies provide important characteristics about driver distractions, there are open challenges that remain to be addressed. First, there is no standard for quantifying the driver distraction level in either absolute or relative terms. Defining a reliable driver distraction metric is important to train machine learning algorithms that predict driver distractions. To address this question, we propose to use human perception to measure the perceived distraction levels across different types of distractions (e.g. visual and cognitive). In the visual-cognitive distraction space, we define distraction modes to represent the driver distraction level using data driven approaches. This representation provides a more comprehensive description of the detrimental effects caused by secondary tasks. It provides an ideal framework to analyze the effects of future in-vehicle systems on driver behaviors. Second, most of the previous studies have largely relied on driving simulators. Instead, we consider real-world driving scenarios on real roads. We use multimodal features extracted from various noninvasive sensors including the controller area network-bus (CAN-Bus), video cameras and microphone arrays. By applying different machine learning techniques, including binary classification, multiclass classification and regression models, we build models to track driver's attention level, detect driver distraction, and identify multi-modal discriminative features to capture distracted driver behaviors. Finally, we explore the detection of contextual information about the driver and the road to enhance the proposed driver attention model. In particular, we focus on detecting mirror check actions and detection of frontal vehicles. This contextual information can inform the in-vehicle safety system whether the drivers appropriately respond to the driving tasks required by the road conditions. In addition, we also develop a user-independent calibration free gaze estimation model, which is closely related to the driver visual/cognitive distraction estimation.
Author: John Hansen Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110669781 Category : Computers Languages : en Pages : 327
Book Description
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.
Author: Gary L Rupp Publisher: SAE International ISBN: 0768061482 Category : Technology & Engineering Languages : en Pages : 266
Book Description
This book focuses on the study of secondary task demands imposed by in-vehicle devices on the driver while driving. It provides a mechanism for researchers to evaluate how in-vehicle devices such as navigation systems – as well as other devices such as cell phones – affect driver distraction and impact safety. This book, which features the work presented by international experts at the 4th International Driver Metrics Workshop, in June 2008, offers a summary of the current state of driver metrics research. Edited by workshop moderator Dr. Gary L. Rupp, the book introduces vital information to support the design of in-vehicle information and communication systems (IVIS). Topics covered include: • Driver object and event detection • Peripheral detection tasks (PDT) • Tactile-based detection tasks (TDT) • Modified Sternberg method for assessing visual and cognitive load of in-vehicle tasks • Modified Sternberg method for assessing peripheral detection task and lane change tests • The relationship between performance metrics and crash risk • Characterizing driver behaviors observed in naturalist driving studies • Developing metrics from lane change test studies
Author: Management Association, Information Resources Publisher: IGI Global ISBN: 1522579109 Category : Social Science Languages : en Pages : 1765
Book Description
The rapid evolution of technology continuously changes the way people interact, work, and learn. By examining these advances from a sociological perspective, researchers can further understand the impact of cyberspace on human behavior, interaction, and cognition. Multigenerational Online Behavior and Media Use: Concepts, Methodologies, Tools, and Applications is a vital reference source covering the impact of social networking platforms on a variety of relationships, including those between individuals, governments, citizens, businesses, and consumers. The publication also highlights the negative behavioral, physical, and mental effects of increased online usage and screen time such as mental health issues, internet addiction, and body image. Showcasing a range of topics including online dating, smartphone dependency, and cyberbullying, this multi-volume book is ideally designed for sociologists, psychologists, computer scientists, engineers, communication specialists, academicians, researchers, and graduate-level students seeking current research on media usage and its behavioral effects.
Author: Vincent G. Duffy Publisher: Springer Nature ISBN: 3031480473 Category : Computers Languages : en Pages : 661
Book Description
This seven-volume set LNCS 14054-14060 constitutes the proceedings of the 25th International Conference, HCI International 2023, in Copenhagen, Denmark, in July 2023. For the HCCII 2023 proceedings, a total of 1578 papers and 396 posters was carefully reviewed and selected from 7472 submissions. Additionally, 267 papers and 133 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work”. These papers were organized in the following topical sections: HCI Design and User Experience; Cognitive Engineering and Augmented Cognition; Cultural Issues in Design; Technologies for the Aging Population; Accessibility and Design for All; Designing for Health and Wellbeing; Information Design, Visualization, Decision-making and Collaboration; Social Media, Creative Industries and Cultural Digital Experiences; Digital Human Modeling, Ergonomics and Safety; HCI in Automated Vehicles and Intelligent Transportation; Sustainable Green Smart Cities and Smart Industry; eXtended Reality Interactions; Gaming and Gamification Experiences; Interacting with Artificial Intelligence; Security, Privacy, Trust and Ethics; Learning Technologies and Learning Experiences; eCommerce, Digital Marketing and eFinance.
Author: Elmer Dadios Publisher: BoD – Books on Demand ISBN: 9535121537 Category : Computers Languages : en Pages : 182
Book Description
This book is a collection of chapters, concerning the developments within the Fuzzy Logic field of study. The book includes scholarly contributions by various authors pertinent to Fuzzy Logic. Each contribution comes as a separate chapter complete in itself but directly related to the books topics and objectives. The target audience comprises scholars and specialists in the field.
Author: Raghvendra Kumar Publisher: Springer Nature ISBN: 9811575274 Category : Technology & Engineering Languages : en Pages : 975
Book Description
This book comprises select peer-reviewed proceedings of the international conference on Research in Intelligent and Computing in Engineering (RICE 2020) held at Thu Dau Mot University, Vietnam. The volume primarily focuses on latest research and advances in various computing models such as centralized, distributed, cluster, grid, and cloud computing. Practical examples and real-life applications of wireless sensor networks, mobile ad hoc networks, and internet of things, data mining and machine learning are also covered in the book. The contents aim to enable researchers and professionals to tackle the rapidly growing needs of network applications and the various complexities associated with them.
Author: Peter Harrington Publisher: Simon and Schuster ISBN: 1638352453 Category : Computers Languages : en Pages : 558
Book Description
Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce
Author: Jiadi Yu
Author: Jiadi Yu
Author: Landu Jiang
Author: Nanxiang Li
Author: John Hansen
Author: Gary L Rupp
Author: Management Association, Information Resources
Author: Vincent G. Duffy
Author: Elmer Dadios
Author: Raghvendra Kumar
Author: Peter Harrington