scholarly journals Development Of In-Vehicle Collision Warning System For Intersections

2021 ◽  
Author(s):  
Essam M.S.A.E.A. Dabbour
Keyword(s):  
Statistical Models ◽  
Human Error ◽  
Vision System ◽  
Warning System ◽  
Network Models ◽  
Human Vision ◽  
Human Vision System ◽  
Neural Network Models ◽  
Collision Warning ◽  
The Impact

Most of the current collision warning systems are mainly designed to detect imminent rear-end, lane-changing or lane departure collisions. None of them was designed to detect imminent intersection collisions, which were found to cause more fatalities and injuries than other types of collisions. One of the most important factors that lead to intersection collisions is driver’s human error and misjudgement. A main source for human errors is the insensitivity of human vision system to detect the speed and acceleration of approaching vehicles; and therefore, any algorithm for an intersection collision warning system should give consideration to the speed and acceleration of all approaching vehicles to mitigate the inadequacy in the human vision system. Moreover, when designing any collision warning system, false warnings should be minimized to avoid nuisance for drivers that might lead to the loss of the system’s reliability by potential users. This research proposed an intersection collision warning system that utilizes commercially-available detection sensors to detect approaching vehicles and measure their speeds and acceleration rates in order to estimate the time-to-collision and compare it to the time required for the turning vehicle to clear the paths of the approaching vehicles. By comparing these times, the system triggers a warning message if an imminent collision is detected. Minimum specifications for key hardware components are established for the proposed system which does not depend on specific technology. To estimate the time require to clear the paths of the approaching vehicles, statistical models were developed to estimate the perception-reaction time for the driver of the turning vehicle and the rate of acceleration selected when departing the intersection. The statistical models include regression models that were calibrated from data collected through driving simulation and more-sophisticated artificial neural network models that are based on actual data collected from a specific driver on a specific vehicle. The proposed system was validated by computer simulation to verify the accuracy of the developed algorithms and to measure the impact of different components on the functionality and reliability of the system. Final conclusions are provided along with recommendations for further research.

scholarly journals Development Of In-Vehicle Collision Warning System For Intersections

2021 ◽  
Author(s):  
Essam M.S.A.E.A. Dabbour
Keyword(s):  
Statistical Models ◽  
Human Error ◽  
Vision System ◽  
Warning System ◽  
Network Models ◽  
Human Vision ◽  
Human Vision System ◽  
Neural Network Models ◽  
Collision Warning ◽  
The Impact

Most of the current collision warning systems are mainly designed to detect imminent rear-end, lane-changing or lane departure collisions. None of them was designed to detect imminent intersection collisions, which were found to cause more fatalities and injuries than other types of collisions. One of the most important factors that lead to intersection collisions is driver’s human error and misjudgement. A main source for human errors is the insensitivity of human vision system to detect the speed and acceleration of approaching vehicles; and therefore, any algorithm for an intersection collision warning system should give consideration to the speed and acceleration of all approaching vehicles to mitigate the inadequacy in the human vision system. Moreover, when designing any collision warning system, false warnings should be minimized to avoid nuisance for drivers that might lead to the loss of the system’s reliability by potential users. This research proposed an intersection collision warning system that utilizes commercially-available detection sensors to detect approaching vehicles and measure their speeds and acceleration rates in order to estimate the time-to-collision and compare it to the time required for the turning vehicle to clear the paths of the approaching vehicles. By comparing these times, the system triggers a warning message if an imminent collision is detected. Minimum specifications for key hardware components are established for the proposed system which does not depend on specific technology. To estimate the time require to clear the paths of the approaching vehicles, statistical models were developed to estimate the perception-reaction time for the driver of the turning vehicle and the rate of acceleration selected when departing the intersection. The statistical models include regression models that were calibrated from data collected through driving simulation and more-sophisticated artificial neural network models that are based on actual data collected from a specific driver on a specific vehicle. The proposed system was validated by computer simulation to verify the accuracy of the developed algorithms and to measure the impact of different components on the functionality and reliability of the system. Final conclusions are provided along with recommendations for further research.

Development of Warning System and Driving Model for Overtaking Trucks on Two-Lane Highways

2019 ◽  
Vol 2673 (11) ◽  
pp. 1-12 ◽  
Author(s):  
Udai Hassein ◽  
Maksym Diachuk ◽  
Said Easa
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Driving Simulator ◽  
Real Life ◽  
Warning System ◽  
Steering Control ◽  
Collision Warning ◽  
Radar Sensors ◽  
Sight Distance ◽  
The Impact

Passing collisions are one of the most serious traffic safety problems on two-lane highways. These collisions occur when a driver overestimates the available sight distance. This paper presents a framework for a passing collision warning system (PCWS) that assists drivers in avoiding passing collisions by reducing the likelihood of human error. The system uses a combination of a camera and radar sensors to identify the impeding vehicle type and to detect the opposing vehicles traveling in the left lane. The study involved the development of a steering control model providing lane-change maneuvers, the design of a driving simulator experiment that allows for the collection of data necessary to estimate passing parameters, and the elaboration of the algorithm for the PCWS based on sensor signals to detect impeding vehicles such as trucks. Simulation tests were carried out to confirm the effectiveness of the proposed PCWS algorithm. The impact of driver behavior on passing maneuvers was also investigated. Mathematical and imitation models were enhanced to implement Simulink for replications of real-life driving scenarios. The different factors that affect system accuracy were also examined.

scholarly journals Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

2021 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Ali Arishi ◽  
Krishna K Krishnan ◽  
Vatsal Maru
Keyword(s):  
Raw Materials ◽  
Early Stage ◽  
Supply And Demand ◽  
Network Models ◽  
Combine System ◽  
Neural Network Models ◽  
Digital Twin ◽  
Twin Model ◽  
The Impact ◽  
Reaction Patterns

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

Analog Learning Neural Network using Two-Stage Mode by Multiple and Sample Hold Circuits

2014 ◽  
Vol 2 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Masashi Kawaguchi ◽  
Naohiro Ishii ◽  
Takashi Jimbo
Keyword(s):  
Neural Network ◽  
Vision System ◽  
Network Models ◽  
Input Voltage ◽  
Neural Network Models ◽  
Artificial Retina ◽  
The Neural Network ◽  
Connection Coefficient ◽  
Retina Chip ◽  
Short Time

In the neural network field, many application models have been proposed. A neuro chip and an artificial retina chip are developed to comprise the neural network model and simulate the biomedical vision system. Previous analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connection coefficient. In this study, we used analog electronic multiple and sample hold circuits. The connecting weights describe the input voltage. It is easy to change the connection coefficient. This model works only on analog electronic circuits. It can finish the learning process in a very short time and this model will enable more flexible learning.

The Study of Spatial Frequency Channels for Human Visual System

2019 ◽  
Vol 33 (06) ◽  
pp. 1955007
Author(s):  
Xiangyang Xu ◽  
Qiao Chen ◽  
Ruixin Xu
Keyword(s):  
Receptive Field ◽  
Spatial Frequency ◽  
Visual System ◽  
Human Visual System ◽  
Frequency Tuning ◽  
Tuning Curve ◽  
Vision System ◽  
Human Vision ◽  
Psychophysical Experiment ◽  
Human Vision System

Similar to auditory perception of sound system, color perception of the human visual system also presents a multi-frequency channel property. In order to study the multi-frequency channel mechanism of how the human visual system processes color information, the paper proposed a psychophysical experiment to measure the contrast sensitivities based on 17 color samples of 16 spatial frequencies on CIELAB opponent color space. Correlation analysis was carried out on the psychophysical experiment data, and the results show obvious linear correlations of observations for different spatial frequencies of different observers, which indicates that a linear model can be used to model how human visual system processes spatial frequency information. The results of solving the model based on the experiment data of color samples show that 9 spatial frequency tuning curves can exist in human visual system with each lightness, R–G and Y–B color channel and each channel can be represented by 3 tuning curves, which reflect the “center-around” form of the human visual receptive field. It is concluded that there are 9 spatial frequency channels in human vision system. The low frequency tuning curve of a narrow-frequency bandwidth shows the characteristics of lower level receptive field for human vision system, the medium frequency tuning curve shows a low pass property of the change of medium frequent colors and the high frequency tuning curve of a width-frequency bandwidth, which has a feedback effect on the low and medium frequency channels and shows the characteristics of higher level receptive field for human vision system, which represents the discrimination of details.

Modern Furniture Color Image Based on Eye Tracking

2012 ◽  
Vol 157-158 ◽  
pp. 410-414 ◽  
Author(s):  
Ji Feng Xu ◽  
Han Ning Zhang
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Color Image ◽  
Vision System ◽  
Human Vision ◽  
Relative Importance ◽  
Human Vision System ◽  
Eye Tracker ◽  
Color Features ◽  
The Relationship

The relationship between modern furniture color image and eye tracking has been of interest to academics and practitioners for many years. We propose and develop a new view and method exploring these connections, utilizing data from a survey of 31 testees’ eye tracking observed value. Using Tobii X120 eye tracker to analyze eye movement to furniture samples in different hue and tones colors, we highlight the relative importance of the effect of furniture color on human vision system and show that the connections between furniture color features with color image.

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