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Author: Nanxiang Li
Publisher:
ISBN:
Category : Automobile driving
Languages : en
Pages : 262
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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: Nanxiang Li
Publisher:
ISBN:
Category : Automobile driving
Languages : en
Pages : 262
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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: Jinesh Jatan Raj Jain
Publisher:
ISBN:
Category : Distracted driving
Languages : en
Pages : 74
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Book Description
New in-vehicle technologies have been developed in the last decade. As a downside, these advances have led to an increasing number of accidents and crashes due to driver distraction. Therefore, monitoring the attention level in the drivers has become an important research problem. This is precisely the aim of this study. A database containing 16 drivers was collected during real-driving scenarios. The drivers were asked to perform common secondary tasks such as operating in-vehicle technologies (radio, phones and navigation system). The collected database, which includes various noninvasive sensors, is analyzed to identify multimodal features that can be used to discriminate between normal and task driving conditions. These features are then used for binary classification using k-Nearest Neighbors. The performance of the classifier reaches average accuracies of 78.9%, using CAN-bus data and features extracted from a frontal camera. A multi-class classifier using the k-NN algorithm is also built to distinguish between normal and different secondary task conditions. This classifier achieved accuracies of 40.68%, which is significantly higher than chances (12.5%). Motivated by these results, the study models normal and task driving behaviors using Gaussian Mixture Models (GMMs). The purpose of models is to learn normal patterns used by drivers and used this knowledge to measure deviation from normal driving behaviors as an indication of distraction. The study includes task independent and task dependent models. Building upon these previous results, a regression model is proposed to obtain a metric to characterize the attention level of the driver. This metric can be used to signal alarms, preventing collision and improving the overall driving experience.
Author: Huseyin Abut
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 1501504169
Category : Technology & Engineering
Languages : en
Pages : 271
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Book Description
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
Author: John Hansen
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110669781
Category : Computers
Languages : en
Pages : 327
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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: Huseyin Abut
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 311098122X
Category : Technology & Engineering
Languages : en
Pages : 274
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Book Description
This book features works from world-class experts from academia, industry, and national agencies from across the world focusing on a wide spectrum of automotive fields covering in-vehicle signal processing, driver modeling, systems and safety. The essays collected in this volume present cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces.
Author: Sumit Jha
Publisher:
ISBN:
Category : Deep learning (Machine learning)
Languages : en
Pages : 0
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Book Description
Monitoring driver behavior can play a vital role in combating various road hazards. The majority of accidents can be avoided if the driver gets an adequate warning few seconds prior to the event. Monitoring driver actions can provide insights about the driver's intent, attention and vigilance. This information can be helpful in designing smart interfaces in the vehicle that provides necessary warning to the driver or take control when necessary. Visual attention is one of the most important factors in driver monitoring, since most driving maneuvers strongly rely on vision. An inattentive driver may lack awareness about the factors in the environment such as pedestrians, other vehicles and trac changes. Visual attention of a driver can be monitored by either tracking the driver's head pose or by tracking their eye movement. While advancement in computer vision have inspired various studies that can eciently track head and eye movement from the face, these models face challenges in a naturalistic driving environment because of the changes in illumination, high head rotation and occlusions. This dissertation discusses various methods to predict the driver's visual attention using probabilistic visual maps. We collect a large scale multimodal dataset where 59 drivers are recording when performing various secondary activities while driving, to capture the vi diversity of data in a naturalistic driving environment. The subjects fixate their gaze at predetermined location which help us establish a correspondence between the driver's face and their gaze target. Using this dataset, we have performed various analysis that guided our proposed models to predict the driver's visual attention. We establish that while the head pose of the driver has a strong correlation with the driver's visual attention the relationship is not one to one. Hence, it is not feasible to design models that can predict a single value of driver's gaze from the head pose. Therefore, we take a probabilistic approach where the driver's visual attention is predicted as a probabilistic visual map whose value at each point depend on the probability that the driver is looking at a certain direction. First, we design parametric regression models that provide a Gaussian distribution of the driver's gaze from the driver's head pose. The model is heteroscedastic based on Gaussian Process Regression (GPR) which learns the distribution of gaze as a gaussian random process which is function of the head pose in 6 degrees of freedom. Next, we propose deep networks with convolutional and upsampling layers that performs classification on a 2D grid to obtain visual map. The model is non-parametric and learns the distribution from the data. We propose two di↵erent models. The first model takes the head pose of the driver as the input and passes it through a fully connected layer followed by convolution and upsampling to predict the visual attention at di↵erent resolutions. The second model takes an image of the eye patch as an input and passes it through multiple layers of convolution and maxpooling to obtain a low dimensional representation of the visual attention. Consecutively, this low dimensional representation is passed through upsampling and convolution layers to obtain a high dimension representation of visual attention. In our final approach, We design a fusion model that integrates the information from the driver's head pose as well as their eye appearance to predict a visual attention map at multiple resolution. This model follows an encoder-decoder architecture with two encoders, one each for the head pose and the gaze and a decoder that concatenates the information from both the head pose and gaze to obtain the final visual map. We project the model prediction onto the road and evaluate it on data when the subject looks at the landmarks on the road.
Author: Huadong Ma
Publisher: Springer Nature
ISBN: 9811601070
Category : Computers
Languages : en
Pages : 249
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Book Description
Sensor networks are an essential component of the Internet of Things (IoT), and Multimedia Sensor Networks (MSNs) are the most important emerging area in sensor networks. However, multimedia sensing is characterized by diversified modes, large volumes of data, considerable heterogeneity, and complex computing, as a result of which the theory and methods for traditional sensor networks can’t be applied to MSNs. Based on the authors’ years of systematic research on related theory and methods, this book provides a comprehensive review of MSNs. The coverage ranges from networked sensing and fusion-based transmission, to route discovery and in-network computing. The book presents the most important scientific discoveries and fundamental theories on MSNs, while also exploring practical approaches and typical applications. Given its scope, it is especially suitable for students, researchers and practitioners interested in understanding scientific problems involved in characterizing multimedia sensing features, revealing the transmission mechanisms of MSNs, and constructing efficient in-network multimedia computing paradigms. In this book, readers will learn essential methods for achieving the optimal deployment of, efficient and reliable transmission, and timely information processing in MSNs.
Author: Deepak Garg
Publisher: Springer Nature
ISBN: 3030369455
Category : Computers
Languages : en
Pages : 349
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Book Description
This book constitutes the proceedings of the 15th International Conference on Information Systems Security, ICISS 2019, held in Hyderabad, India, in December 2019. The 13 revised full papers and 4 short papers presented in this book together with 4 abstracts of invited talks were carefully reviewed and selected from 63 submissions. The papers cover topics such as: smart contracts; formal techniques; access control; machine learning; distributed systems; cryptography; online social networks; images and cryptography.
Author: John H.L. Hansen
Publisher: Springer Science & Business Media
ISBN: 1441996079
Category : Technology & Engineering
Languages : en
Pages : 332
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Book Description
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.
Author: Ning Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 125
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Book Description
As vehicle automation becomes increasingly prevalent and capable, drivers have the opportunity to delegate primary driving task control to automated systems. In recent years, significant efforts have been placed on developing and deploying Advanced Driver Assistance Systems (ADAS). These systems are designed to work with human drivers to increase vehicle safety, control, and performance in both ordinary and emergent situations. Current ADAS are mainly presented in rule-based or manually programmed design based on the summary and modeling of pre-collected human performance data. However, the pre-fixed system with limited personalization may not match human drivers' needs, which may arise the driver's dissatisfaction and cause ineffective system improvement. Human-centered machine learning (HCML) includes explicitly recognizing this human operator's role, as well as re-constructing machine learning workflows based on human working practices. The goal of this dissertation is to build a novel driver behavior modeling framework to understand and predict interactions with the driver assistance system from a human-centered perspective. It can lead not only to more usable machine learning tools but to new ways of improving the driver assistance systems. A driving simulator study was conducted to evaluate drivers' interactions with Forward Collision Warning (FCW) system. Gaussian Mixture Model (GMM) clusterization was used to identify different driving styles based drivers' driving performance, secondary task engagement, eye glance behavior and survey information. The impact of the FCW system on the different driving styles was also evaluated and discussed from three perspectives: initial reaction, distraction types, and safety benefits. A driver behavior model was also built using inverse reinforcement learning. Lastly, the timing prediction of FCW using driving preference was compared to the algorithm from a traditional FCW system. The findings of this study showed that ADAS without human feedback may not always bring positive safety benefits. Learning driver's preference through inverse reinforcement learning could better account for future scenarios and better predict driver behavior (e.g., braking action). This algorithm can be incorporated into real world in-vehicle warning systems such that the feedback and driving styles of the human operator are appropriately considered.
