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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: 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: 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: Sumit Jha
Publisher:
ISBN:
Category : Automobile drivers
Languages : en
Pages : 106
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Book Description
Monitoring driver behaviors is crucial in the design of assistance systems that can detect drivers' actions, providing necessary warnings when needed. The visual attention of a driver is an important aspect to consider, as most driving tasks require visual resources. Studying the behaviors of the driver can help in understanding the focus of attention and his/her capability to perform driving-related tasks. Estimating gaze on vehicle environments is a challenging problem due to changes in illumination, head rotations, and occlusions. This thesis explores the relationship between head pose and gaze during natural driving conditions. We collect a corpus in naturalistic driving environment, where 16 subjects were asked to look at predetermined marker locations. The task was conducted while the participants were driving, and when the vehicle was parked. We use a headband with multiple AprilTags to determine the ground truth head pose, which were compared with the results of a state-of-the-art head pose estimation algorithm. We design regression models to study the predictability of the gaze when the orientation and position of the driver's head is known. We study the amount of eye movement bias during glance actions when looking in different directions. Finally, we propose a probabilistic model to predict a visual salient map providing confidence scores about potential gaze directions. Using this model, we project the saliency map into the road scene to identify potential objects that the driver is focusing his/her attention. This thesis, therefore, contributes to an improved understanding of head pose and eye gaze and how characterizing this knowledge could improve driver safety systems.
Author: A. L. Yarbus
Publisher: Springer
ISBN: 1489953795
Category : Medical
Languages : en
Pages : 234
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Book Description
Author: Shane Parrish
Publisher: Penguin
ISBN: 0593719972
Category : Business & Economics
Languages : en
Pages : 209
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Book Description
Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.
Author: Michael D. Matthews
Publisher:
ISBN: 0190455136
Category : Medical
Languages : en
Pages : 489
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Book Description
Human Performance Optimization: The Science and Ethics of Enhancing Human Capabilities explores current and emerging strategies for enhancing individual and team performance, especially in high-stakes, stressful settings such as the military, law enforcement, firefighting, or competitive corporate settings. Taking a cognitive neuroscience perspective, scientifically grounded approaches to optimizing human performance are explored in depth.
Author: Shaoshan Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681731673
Category : Computers
Languages : en
Pages : 285
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Book Description
This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.
Author: Mark H. Johnson
Publisher: John Wiley & Sons
ISBN: 0470752025
Category : Psychology
Languages : en
Pages : 560
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Book Description
The first edition of this successful reader brought together key readings in the area of developmental cognitive neuroscience for students. Now updated in order to keep up with this fast moving field, the volume includes new readings illustrating recent developments along with updated versions of previous contributions.
Author: Intergovernmental Panel on Climate Change (IPCC)
Publisher: Cambridge University Press
ISBN: 9781009157971
Category : Science
Languages : en
Pages : 755
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Book Description
The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for assessing the science related to climate change. It provides policymakers with regular assessments of the scientific basis of human-induced climate change, its impacts and future risks, and options for adaptation and mitigation. This IPCC Special Report on the Ocean and Cryosphere in a Changing Climate is the most comprehensive and up-to-date assessment of the observed and projected changes to the ocean and cryosphere and their associated impacts and risks, with a focus on resilience, risk management response options, and adaptation measures, considering both their potential and limitations. It brings together knowledge on physical and biogeochemical changes, the interplay with ecosystem changes, and the implications for human communities. It serves policymakers, decision makers, stakeholders, and all interested parties with unbiased, up-to-date, policy-relevant information. This title is also available as Open Access on Cambridge Core.
