Risk Assessments And Modeling Of Driver By Using Risk Potential Theory
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| Author | : Riku Kikuta |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Recently, various self-driving and driving assistance systems such as Advanced Driver Assistance System (ADAS) have been developed with the intent to reduce the number of motor vehicle accidents. While self-driving systems have been proven to reduce traffic accidents, the systems sometimes make other drivers confused because of their mechanical behavior. To avoid confusion and possible error, it is necessary to construct self-driving systems that exhibit human-like behaviors. Risk Potential theory has been used to construct models that successfully represent driver behavior, especially expert behavior. This project uses Risk Potential theory to construct and evaluate a collision avoidance driver model which uses braking to avoid potential collisions with pedestrians. As a first step, a basic driver model which uses Risk Potential theory is constructed and evaluated using metrics such as collision avoidance, comfortability, and false alarm avoidance. Second, human driving data is collected to observe driver's risk perception during interactions with a pedestrian. Finally, our proposed driver models improve on standard RP model's performance but comparisons of the models with observed human performance reveal opportunities for further improvement.
| Author | : Xiaolin Song |
| Publisher | : Springer Nature |
| Total Pages | : 160 |
| Release | : 2022-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3031015096 |
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.
| Author | : Andre J. E. Levesque |
| Publisher | : |
| Total Pages | : |
| Release | : 2012 |
| Genre | : |
| ISBN | : 9780494763858 |
| Author | : Ja Young Lee |
| Publisher | : |
| Total Pages | : 165 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Driver distraction has been a longstanding cause of vehicle crashes. To understand and predict distracted drivers' task switching behavior between driving and secondary task and to mitigate negative consequences of distraction associated with inappropriate task switching, researchers have analyzed glance behaviors and developed models of driver behavior. In most of the studies, however, the microstructure of glance patterns that shows the process of drivers' engagement and disengagement with secondary task has been neglected. This dissertation presents a computational model of driver distraction that accounts for the joint and dynamic influence of external factors (influence of uncertainty and task structure) and internal factors (individual differences), which has been not well demonstrated in previous driver models. The model can predict drivers' glance patterns associated with a secondary task, making it possible to quickly evaluate potential danger of many candidate task designs and to better understand cognitive mechanisms underlying distraction. The model specifically focuses on the effect of task structures (i.e., subtask boundary designs) on dynamic task switching behavior. The model shows how subtask boundaries influence how frequently and how long drivers--each with different characteristics--interact with secondary tasks. Another gap of the current literature is that the actual crash risk that different task structures contribute to driver distraction has not been extensively studied. This dissertation also estimates crash and injury risk associated with different subtask boundary designs, using a counterfactual simulation, which explores how alternative glance patterns might alter the observed consequence of an event. Overall, the dissertation spans an empirical study (Chapter 3), computational modeling of task switching behavior (Chapter 4), and simulation of crash risk (Chapter 5). This set of studies explores the effect of task structures on glance patterns and quantifies the risk of driver distraction caused by different task structures. The model's approach of inferring cognitive mechanisms (micro-level) underlying the overall system outcomes (macro-level) is rigorous and useful in understanding the overall cost or benefit of task switching, not only in driving situations but also in other multitasking situations with time-critical tasks. The computational model and simulation may also be used to assess how a task affects multitasking performance in the early stage of task design.
| Author | : Lesley Walls |
| Publisher | : CRC Press |
| Total Pages | : 4767 |
| Release | : 2016-11-25 |
| Genre | : Technology & Engineering |
| ISBN | : 1315349167 |
The safe and reliable performance of many systems with which we interact daily has been achieved through the analysis and management of risk. From complex infrastructures to consumer durables, from engineering systems and technologies used in transportation, health, energy, chemical, oil, gas, aerospace, maritime, defence and other sectors, the management of risk during design, manufacture, operation and decommissioning is vital. Methods and models to support risk-informed decision-making are well established but are continually challenged by technology innovations, increasing interdependencies, and changes in societal expectations. Risk, Reliability and Safety contains papers describing innovations in theory and practice contributed to the scientific programme of the European Safety and Reliability conference (ESREL 2016), held at the University of Strathclyde in Glasgow, Scotland (25—29 September 2016). Authors include scientists, academics, practitioners, regulators and other key individuals with expertise and experience relevant to specific areas. Papers include domain specific applications as well as general modelling methods. Papers cover evaluation of contemporary solutions, exploration of future challenges, and exposition of concepts, methods and processes. Topics include human factors, occupational health and safety, dynamic and systems reliability modelling, maintenance optimisation, uncertainty analysis, resilience assessment, risk and crisis management.
| Author | : Banghee So |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Powered with telematics technology, insurers can now capture a wide range of data to better decode driver's behavior, such as distance traveled and how drivers brake, accelerate or make turns. Such additional information helps insurers improve risk assessments for usage-based insurance (UBI), an increasingly popular industry innovation. In this thesis, we first explore how to integrate telematics information to improve understanding of driver heterogeneity, as well as to better predict accident counts. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero accidents, a less proportion with exactly one accident, and far fewer with two or more accidents. We introduce the use of a cost-sensitive multi-class adaptive boosting algorithm, which we call SAMME.C2, to handle such imbalances in a classification model. Using the SAMME.C2 algorithm, we find improved assessment of driving behavior with telematics relative to traditional risk variables. We next demonstrate the theoretical justification of the SAMME.C2 algorithm in two respects: (1) it is equivalent to Forward Stagewise Additive Modeling with exponential loss, and (2) it is a Bayes classifier. When cost-sensitive learning is added, we find the superiority of SAMME.C2 in controlling for issues related to class imbalances, especially when compared to just the SAMME algorithm. We performed numerical experiments to better understand the distinguishing characteristics of the algorithm. Finally, this thesis describes the techniques employed in the production of a synthetic dataset of driver telematics that is emulated from a real insurance dataset. The method uses a three-stage process that involves deploying machine learning algorithms. It is aimed to produce a resource that can be used to advance models to assess risks for usage-based insurance. It is the hope of this work to provide and encourage the research community to explore innovative methods relevant to such data. The synthetic dataset produced includes 100,000 observations about driver's claims experience (both claim counts and amounts were generated) together with associated classical risk variables and telematics-related variables. We further show, using visualization, model fitting, and data summarization, how remarkable the similarities are between the synthetic and the real datasets.
| Author | : Martin Rosenberger |
| Publisher | : CRC Press |
| Total Pages | : 1644 |
| Release | : 2016-03-30 |
| Genre | : Technology & Engineering |
| ISBN | : 1498777023 |
The IAVSD Symposium is the leading international conference in the field of ground vehicle dynamics, bringing together scientists and engineers from academia and industry. The biennial IAVSD symposia have been held in internationally renowned locations. In 2015 the 24th Symposium of the International Association for Vehicle System Dynamics (IAVSD)
| Author | : Risto Näätänen |
| Publisher | : North-Holland |
| Total Pages | : 288 |
| Release | : 1976 |
| Genre | : Transportation |
| ISBN | : |
| Author | : Carlo Cacciabue |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 441 |
| Release | : 2010-04-28 |
| Genre | : Computers |
| ISBN | : 1846286182 |
This book presents a general overview of the various factors that contribute to modelling human behaviour in automotive environments. This long-awaited volume, written by world experts in the field, presents state-of-the-art research and case studies. It will be invaluable reading for professional practitioners graduate students, researchers and alike.
| Author | : Zhigang Fang |
| Publisher | : CRC Press |
| Total Pages | : 794 |
| Release | : 2016-10-14 |
| Genre | : Science |
| ISBN | : 1498779220 |
The 2016 International Conference on Energy Science and Applied Technology (ESAT 2016) held on June 25-26 in Wuhan, China aimed to provide a platform for researchers, engineers, and academicians, as well as industrial professionals, to present their research results and development activities in energy science and engineering and its applied technology. The themes presented in Energy Science and Applied Technology ESAT 2016 are: Technologies in Geology, Mining, Oil and Gas; Renewable Energy, Bio-Energy and Cell Technologies; Energy Transfer and Conversion, Materials and Chemical Technologies; Environmental Engineering and Sustainable Development; Electrical and Electronic Technology, Power System Engineering; Mechanical, Manufacturing, Process Engineering; Control and Automation; Communications and Applied Information Technologies; Applied and Computational Mathematics; Methods and Algorithms Optimization; Network Technology and Application; System Test, Diagnosis, Detection and Monitoring; Recognition, Video and Image Processing.
