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Modeling Pipeline Driving Behaviors
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Book Synopsis Modeling Pipeline Driving Behaviors by : Zou, Xi
Download or read book Modeling Pipeline Driving Behaviors written by Zou, Xi and published by . This book was released on 2008 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Driving behaviors at intersection are complex because drivers have to perceive more traffic events than normal road driving and thus are exposed to more errors with safety consequences. Drivers make real-time responsesin a stochastic manner. This paper presents our study using Hidden Markov Models (HMM) to model driving behaviors at intersections. Observed vehicle movement data are used to build up the model. A single HMM is used to cluster the vehicle movements when they are close to intersection. The re-estimated clustered HMMs provide better prediction of the vehicle movements compared to traditional car-following models. Only through vehicles on major roads are considered in this paper. Journal of the Transportation Research Board: Transportation.
Book Synopsis Behavior Analysis and Modeling of Traffic Participants by : Xiaolin Song
Download or read book Behavior Analysis and Modeling of Traffic Participants written by Xiaolin Song and published by Springer Nature. This book was released on 2022-06-01 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Transportation Research by : Tom V. Mathew
Download or read book Transportation Research written by Tom V. Mathew and published by Springer Nature. This book was released on 2019-10-24 with total page 905 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 4th Conference of the Transportation Research Group of India. It provides a comprehensive analysis of themes spanning the field of transportation encompassing economics, financial management, social equity, green technologies, operations research, big data analysis, econometrics and structural mechanics. This volume will be of interest to researchers, educators, practitioners, managers, and policy-makers world-wide.
Book Synopsis Integrating Driving Behavior Modeling by : Tomer Toledo
Download or read book Integrating Driving Behavior Modeling written by Tomer Toledo and published by . This book was released on 2003 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modeling of Driver Behavior in Real World Scenarios Using Multiple Noninvasive Sensors by : Nanxiang Li
Download or read book Modeling of Driver Behavior in Real World Scenarios Using Multiple Noninvasive Sensors written by Nanxiang Li and published by . This book was released on 2015 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Deep Learning-based Driver Behavior Modeling and Analysis by : Chaojie Ou
Download or read book Deep Learning-based Driver Behavior Modeling and Analysis written by Chaojie Ou and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have increased drastically over the years. In the past decades, there has been a remarkable push towards designing and developing new driver assistance systems with much better recognition and prediction capabilities. Equipped with various sensory systems, these Advanced Driver Assistance Systems (ADAS) are able to accurately perceive information on road conditions, predict traffic situations, estimate driving risks, and provide drivers with imminent warnings and visual assistance. In this thesis, we focus on two main aspects of driver behavior modeling in the design of new generation of ADAS. We first aim at improving the generalization ability of driver distraction recognition systems to diverse driving scenarios using the latest tools of machine learning and connectionist modeling, namely deep learning. To this end, we collect a large dataset of images on various driving situations of drivers from the Internet. Then we introduce Generative Adversarial Networks (GANs) as a data augmentation tool to enhance detection accuracy. A novel driver monitoring system is also introduced. This monitoring system combines multi-information resources, including a driver distraction recognition system, to assess the danger levels of driving situations. Moreover, this thesis proposes a multi-modal system for distraction recognition under various lighting conditions and presents a new Convolutional Neural Network (CNN) architecture, which can operate real-time on a resources-limited computational platform. The new CNN is built upon a novel network bottleneck of Depthwise Separable Convolution layers. The second part of this thesis focuses on driver maneuver prediction, which infers the direction a driver will turn to before a green traffic light is on and predicts accurately whether or not he/she will change the current driving lane. Here, a new method to label driving maneuver records is proposed, by which driving feature sequences for the training of prediction systems are more closely related to their labels. To this end, a new prediction system, which is based on Quasi-Recurrent Neural Networks, is introduced. In addition, and as an application of maneuver prediction, a novel driving proficiency assessment method is proposed. This method exploits the generalization abilities of different maneuver prediction systems to estimate drivers' driving abilities, and it demonstrates several advantages against existing assessment methods. In conjunction with the theoretical contribution, a series of comprehensive experiments are conducted, and the proposed methods are assessed against state-of-the-art works. The analysis of experimental results shows the improvement of results as compared with existing techniques.
Book Synopsis Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation by : Ahura Jami
Download or read book Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation written by Ahura Jami and published by . This book was released on 2018 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network.
Book Synopsis Social, Cultural, and Behavioral Modeling by : Robert Thomson
Download or read book Social, Cultural, and Behavioral Modeling written by Robert Thomson and published by Springer Nature. This book was released on 2021-07-03 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 14th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2021, which was held online during July 6–9, 2021. The 32 full papers presented in this volume were carefully reviewed and selected from 56 submissions. The papers were organized in topical sections as follows: COVID-related focus; methodologies; social cybersecurity and social networks; and human and agent modeling. They represent a wide number of disciplines including computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science. Numerous types of computational methods are used including, but not limited to, machine learning, language technology, social network analysis and visualization, agent-based simulation, and statistics.
Book Synopsis Integrated Model of Driving Behavior by : Tomer Toledo
Download or read book Integrated Model of Driving Behavior written by Tomer Toledo and published by VDM Publishing. This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driving behavior models, which focus on acceleration, lane changing and gap acceptance decisions are essential to microscopic traffic simulation models and important to several other fields of transportation science and engineering. In this book, a framework for driving behavior modeling that integrates the various decisions driver make is developed, implemented and tested. This framework is based on the concepts of short-term goal and short-term plan, which captures inter-dependencies among the various decisions a driver makes as well as their planning and anticipation capabilities. Based on these concepts an integrated driving behavior model that captures both acceleration and lane changing behaviors is specified, estimated using detailed trajectory data and implemented and validated within a microscopic traffic simulation model. This book is addressed to modelers in transportation and traffic engineering and researchers and developers of traffic simulation models.
Book Synopsis Modeling Driving Behavior at Traffic Control Devices by : Abhisek Mudgal
Download or read book Modeling Driving Behavior at Traffic Control Devices written by Abhisek Mudgal and published by . This book was released on 2011 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modeling Human Driving Behavior by : Dimitrios Koutentakis
Download or read book Modeling Human Driving Behavior written by Dimitrios Koutentakis and published by . This book was released on 2020 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this thesis paper is to explore models that can predict and anticipate driver behaviors on the road and give probabilities on future actions of neighboring vehicles, while being lightweight enough to be formally verifiable. This thesis starts with looking into related work and doing a short literature review on previous work on driver models. We then talk about the available datasets used to perform such work, different models used (from classic regressions to neural networks) and finally present my approach and my results.
Book Synopsis Computer Vision – ECCV 2022 by : Shai Avidan
Download or read book Computer Vision – ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-11-02 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Download or read book The Prevention Pipeline written by and published by . This book was released on 1994 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Methods to Explore Driving Behavior Heterogeneity Using SHRP2 Naturalistic Driving Study Trajectory-level Driving Data by : Britton Elaine Hammit
Download or read book Methods to Explore Driving Behavior Heterogeneity Using SHRP2 Naturalistic Driving Study Trajectory-level Driving Data written by Britton Elaine Hammit and published by . This book was released on 2018 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding driving behavior and its impact on traffic flow is crucial for maintaining and operating the transportation network. Traffic analysis requires accurate representations of driving behavior—how different drivers drive and how the same driver adjusts to different driving scenarios—for the realistic development of predictive models. Heterogeneity in driving behavior impacts the capacity of the transportation network; therefore, it is crucial to account for this heterogeneity when planning, assessing alternatives, and managing real-time roadway operations. The recent availability of trajectory-level driving data offers researchers and practitioners an unprecedented opportunity to improve the depiction of driving behavior in microsimulation models. A review of literature clearly demonstrates a foundation for research in heterogeneous driving behaviors, yet countless unanswered questions and uninvestigated hypotheses remain. This dissertation is designed to connect the dots between the complex layers of theory, high resolution driving data, and behavioral analytics necessary for successful behavioral research. Starting with the formation of a hypothesis, this dissertation walks through the required steps for collecting data, processing those data, and analyzing driving behavior. At each pivotal point, contributions are made to bridge the gaps between the crucial elements of research, aspiring to add value to current and future studies. These contributions include (i) trajectory-level data sufficiency guidance, (ii) radar-vision data processing algorithms for instrumented vehicle data, (iii) recommendations for transparent and systematic procedures to calibrate car-following models, (iv) a trajectory simulation validation methodology for interpretation and validation of calibration results, and (v) an empirical car-following model developed from an Artificial Neural Network. Ultimately, an analytic framework is developed from these contributions and applied to trajectory-level data available through the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study to investigate the influence of weather conditions on driving behavior. This case study exemplifies the impact that complex human behaviors have in traffic flow theory and the importance of using trajectory-level data to accurately calibrate driving behavior used in microsimulation models.
Book Synopsis Modeling Methods for Medical Systems Biology by : María Elena Álvarez-Buylla Roces
Download or read book Modeling Methods for Medical Systems Biology written by María Elena Álvarez-Buylla Roces and published by Springer. This book was released on 2018-08-03 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contributes to better understand how lifestyle modulations can effectively halt the emergence and progression of human diseases. The book will allow the reader to gain a better understanding of the mechanisms by which the environment interferes with the bio-molecular regulatory processes underlying the emergence and progression of complex diseases, such as cancer. Focusing on key and early cellular bio-molecular events giving rise to the emergence of degenerative chronic disease, it builds on previous experience on the development of multi-cellular organisms, to propose a mathematical and computer based framework that allows the reader to analyze the complex interplay between bio-molecular processes and the (micro)-environment from an integrative, mechanistic, quantitative and dynamical perspective. Taking the wealth of empirical evidence that exists it will show how to build and analyze models of core regulatory networks involved in the emergence and progression of chronic degenerative diseases, using a bottom-up approach.
Book Synopsis Modelling Behaviour by : Mette Ramsgaard Thomsen
Download or read book Modelling Behaviour written by Mette Ramsgaard Thomsen and published by Springer. This book was released on 2015-11-12 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reflects and expands on the current trend in the building industry to understand, simulate and ultimately design buildings by taking into consideration the interlinked elements and forces that act on them. This approach overcomes the traditional, exclusive focus on building tasks, while posing new challenges in all areas of the industry from material and structural to the urban scale. Contributions from invited experts, papers and case studies provide the reader with a comprehensive overview of the field, as well as perspectives from related disciplines, such as computer science. The chapter authors were invited speakers at the 5th Symposium "Modelling Behaviour", which took place at the CITA in Copenhagen in September 2015.
Book Synopsis Longitudinal Driving Behavior by : Saskia Josephina Leontine Ossen
Download or read book Longitudinal Driving Behavior written by Saskia Josephina Leontine Ossen and published by . This book was released on 2008 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
