New fuzzy solution for determining anticipation and evaluation behavior during car-following maneuvers

2017 ◽  
Vol 232 (7) ◽  
pp. 936-945 ◽  
Author(s):  
Ali Ghaffari ◽  
Alireza Khodayari ◽  
Ali Kamali ◽  
Farzam Tajdari ◽  
Niloofar Hosseinkhani
Keyword(s):  
Evaluation Model ◽  
Fuzzy Model ◽  
Intelligent Vehicles ◽  
Lane Change ◽  
Car Following ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Driving Assistance ◽  
Important Means ◽  
Two Stages

Nowadays, vehicles are the most important means of transportation in our daily lifes. During the last few decades, many studies have been carried out in the field of intelligent vehicles and significant results on the behavior of car-following and lane-change maneuvers have been achieved. However, the effects of lane-change on the car-following models have been relatively neglected. This effect is a temporary state in car-following behavior during which the follower vehicle considerably deviates from conventional car-following models for a limited time. This paper aims to investigate the behavior of the immediate follower during the lane-change of its leader vehicle. Based on a closer inspection of the microstructure behavior of real drivers, this temporary state is divided into two stages of anticipation and evaluation. Afterwards, a novel and adaptive neuro-fuzzy model that considers human driving factors is proposed to simulate the behavior of real drivers. Comparison between model results and real traffic data reveals that the proposed model can describe anticipation and evaluation behavior with smaller errors. The anticipation and evaluation model can modify current car-following models so as to accurately simulate the behavior of an immediate follower which leads to an enhancement of car-following applications such as driving assistance and collision avoidance systems.

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

scholarly journals Fuzzy Model for Quantitative Assessment of Environmental Start-up Projects in Air Transport

2019 ◽  
Vol 16 (19) ◽  
pp. 3585 ◽  
Author(s):  
Miroslav Kelemen ◽  
Volodymyr Polishchuk ◽  
Beáta Gavurová ◽  
Stanislav Szabo ◽  
Róbert Rozenberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Evaluation Model ◽  
Fuzzy Model ◽  
State Funding ◽  
Air Transport ◽  
Fuzzy Evaluation ◽  
Applied Model ◽  
Start Up ◽  
Neuro Fuzzy ◽  
Start Ups

The purpose of this paper is to develop an applied fuzzy model of information technology to obtain quantitative estimates of environmental start-up projects in air transport. The developed model will become a useful tool for venture funds, business angels, or crowdfunding platforms for the development of innovative air transport businesses. Obtaining a quantitative estimate of the environmental start-up projects will increase the sustainability of the decision making on the security of financing of such projects by investors. This article develops a fuzzy evaluation model of project start-ups in air transport as an application of our neuro-fuzzy model in a specific air transport environment. The applied model provides output ranking of start-up project teams in air transport based on a four-layer neuro-fuzzy network. The presented model declares the possibilities of the application to solve these economic problems and offers the space for subsequent research focused on its usability in several areas of start-up development, in sectors and processes differentiated. The benefits are also visible for several types of policies, with an emphasis on decision-making processes in regulatory mechanisms to support the state funding in Slovakia, the EU etc.

scholarly journals A NEURO FUZZY MODEL FOR THE INVESTIGATION OF DETERIORATION OF METALLIC PIPE CONVEYING FLUID UNDER DIFFERENT PIPE BURIAL DEPTH, SOIL TYPES AND PROPERTIES

2016 ◽  
Vol 36 (1) ◽  
pp. 72-79
Author(s):  
TT Akano ◽  
OA Fakindele ◽  
HE Mgbemere ◽  
JC Amechi
Keyword(s):  
Soil Properties ◽  
Membership Function ◽  
Fuzzy Model ◽  
Soil Types ◽  
Burial Depth ◽  
Pipe Conveying Fluid ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Conveying Fluid

Several factors may contribute directly or indirectly to the structural failure of metallic pipes. The most important of which is corrosion. Corrosivity of pipes is not a directly measurable parameter as pipe corrosion is a very random phenomenon. The main aim of the present study is to develop a neuro-fuzzy model capable of establishing corrosion rate criterion as a function of pipe burial depth, soil types, and properties for the prediction of deterioration of metallic pipe conveying fluid. The proposed model includes a fuzzy model and the artificial neural network (ANN) to determine soil corrosivity potential (CoP) based on soil properties. The combination contains the data of linguistic variables characterising various soil properties, and learning capability of the system that constructs relationships among those soil properties and CoP. Subsequently, the artificial neuro-fuzzy inference system (ANFIS) maps each element of its input membership function to an output membership function between 0 and 1 to determine the deterioration rate (CoP) of metallic fluid-conveying-pipe. Field data from buried fluid pipes were examined to illustrate the application of the proposed model. The ultimate goal is the ability to access the current and future life of oil pipe, given a set of circumstances, and also appropriate adoptable methodology in view of a preventive maintenance measure for the pipes in a given operating environment. Results reveal that with more than 40% clay content quickens corrosion of buried fluid pipes more than any other considered factor. http://dx.doi.org/10.4314/njt.v36i1.10

scholarly journals Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

2017 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Ravi Kumar Sharma ◽  
Dr. Parul Gandhi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Software Systems ◽  
Data Sets ◽  
Component Reliability ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Estimate Reliability

There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

DEVELOPMENT OF A NEURO FUZZY MODEL FOR NOISE PREDICTION IN OPENCAST MINES

2009 ◽  
Vol 17 (05) ◽  
pp. 729-745 ◽  
Author(s):  
SANTOSH KUMAR NANDA ◽  
DEBI PRASAD TRIPATHY ◽  
SARAT KUMAR PATRA
Keyword(s):  
Sound Pressure ◽  
Prediction Models ◽  
Fuzzy Model ◽  
Complex Model ◽  
Noise Prediction ◽  
Opencast Mining ◽  
Noise Levels ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Opencast Mines

The need of developing appropriate noise prediction models for finding out the accurate status of noise levels (>90 dBA) generated from various opencast mining machineries is overdue. The measured sound pressure levels (SPL) of equipments are not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular noise prediction models e.g. VDI and ENM have been applied in mining and allied industries. Among these models, VDI2714 is simple and less complex model. In this paper, a neuro-fuzzy model is proposed to predict the machinery noise in an opencast coal mine. The proposed model is trained with VDI2714 and the model output is seen very closely to matching with VDI2714 output. The model proposed has a mean square error of 2.73%. This model takes CPU time of nearly 0.0625 sec where as it takes 0.5 sec for VDI2714 i.e. approximately twelve times faster.

A NEURO-FUZZY MODEL FOR DIMENSIONALITY REDUCTION AND ITS APPLICATION

2007 ◽  
Vol 15 (05) ◽  
pp. 571-593 ◽  
Author(s):  
VITALIY KOLODYAZHNIY ◽  
FRANK KLAWONN ◽  
KATHARINA TSCHUMITSCHEW
Keyword(s):  
Dimensionality Reduction ◽  
Treatment Plant ◽  
Fuzzy Model ◽  
Principal Component ◽  
Nonlinear Dimensionality Reduction ◽  
Training Algorithms ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Plant Data ◽  
Hidden Layer

A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro-Fuzzy Kolmogorov's Network (NFKN) with a “bottleneck” hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an experiment in nonlinear principal component analysis and an application in the visualization of high-dimensional wastewater treatment plant data.

scholarly journals Risky Driving Behavior Recognition Based on Vehicle Trajectory

2021 ◽  
Vol 18 (23) ◽  
pp. 12373
Author(s):  
Shengdi Chen ◽  
Qingwen Xue ◽  
Xiaochen Zhao ◽  
Yingying Xing ◽  
Jian John Lu
Keyword(s):  
Risk Evaluation ◽  
Evaluation Model ◽  
Driving Behavior ◽  
Trajectory Data ◽  
Behavior Recognition ◽  
Risky Driving ◽  
Car Following ◽  
Proposed Model ◽  
Behavior Based ◽  
Risk Evaluation Model

This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.

scholarly journals An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4011 ◽  
Author(s):  
Óscar Mata-Carballeira ◽  
Jon Gutiérrez-Zaballa ◽  
Inés del Campo ◽  
Victoria Martínez
Keyword(s):  
High Speed ◽  
Measurement Unit ◽  
Driving Style ◽  
Intelligent Sensor ◽  
Car Following ◽  
Neuro Fuzzy ◽  
Driving Assistance ◽  
Field Programmable ◽  
Assistance Systems ◽  
On Chip

Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.

scholarly journals A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5139 ◽  
Author(s):  
Huei-Yung Lin ◽  
Jyun-Min Dai ◽  
Lu-Ting Wu ◽  
Li-Qi Chen
Keyword(s):  
Traffic Accidents ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Driving Safety ◽  
Lane Change ◽  
Verification Method ◽  
Vehicle Identification ◽  
Driving Assistance ◽  
Forward Collision Warning ◽  
Self Driving Cars

One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

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