Driver`s Steering Behaviour Identification and Modelling in Near Rear-End col...TELKOMNIKA JOURNAL
This paper studies and identifies driver`s steering manoeuvre behaviour in near rearend
collision. Time-To-Collision (TTC) is utilized in defining driver’s emergency threat
assessment. The target scenario is set up under real experimental environment and the
naturalistic data from the experiment are collected. Four normal drivers are employed for the
experiment to perform the manoeuvre. Artificial Neural Network (ANN) is proposed to model the
behaviour of the driver`s steering manoeuvre. The results show that all drivers manage to
perform steering manoeuvre within the safe TTC region and the modelling results from ANN are
reasonably positive. With further studies and improvements, this model would benefit to
evaluate the driving reliability to enhance traffic safety and Intelligent Transportation System.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Driver`s Steering Behaviour Identification and Modelling in Near Rear-End col...TELKOMNIKA JOURNAL
This paper studies and identifies driver`s steering manoeuvre behaviour in near rearend
collision. Time-To-Collision (TTC) is utilized in defining driver’s emergency threat
assessment. The target scenario is set up under real experimental environment and the
naturalistic data from the experiment are collected. Four normal drivers are employed for the
experiment to perform the manoeuvre. Artificial Neural Network (ANN) is proposed to model the
behaviour of the driver`s steering manoeuvre. The results show that all drivers manage to
perform steering manoeuvre within the safe TTC region and the modelling results from ANN are
reasonably positive. With further studies and improvements, this model would benefit to
evaluate the driving reliability to enhance traffic safety and Intelligent Transportation System.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
Shared Steering Control between a Driver and an Automation: Stability in the ...paperpublications3
Abstract: Now-a-days the Automatic control has been increasingly implemented for vehicle control system. Especially the steering control is essential for preventing accidents. In the existing systems there is no fully automatic steering control and it has serious problems. When it is made automatic, the system complexity is more. So, the shared steering concept is used in the proposed system to avoid accidents. In this, the position of the road is found using the web camera installed in front of the vehicle which is connected to the PC installed with MATLAB. Using MATLAB the image is processed to check the road characteristics. This paper presents an advanced driver assistance system (ADAS) for lane keeping, together with an analysis of its performance and stability with respect to variations in driver behavior. The automotive ADAS proposed is designed to share control of the steering wheel with the driver in the best possible way. Its development was derived from an H2-Preview optimization control problem, which is based on a global driver–vehicle–road (DVR) system. The DVR model makes use of a cybernetic driver model to take into account any driver–vehicle interactions. Such a formulation allows 1) Considering driver assistance cooperation criteria in the control synthesis, 2) improving the performance of the assistance as a cooperative copilot, and 3) analyzing the stability of the whole system in the presence of driver model uncertainty. The developed assistance system improved lane-keeping performance and reduced the risk of a lane departure accident. Good results were obtained using several criteria for human–machine cooperation. Poor stability situations were successfully avoided due to the robustness of the whole system, in spite of a large range of driver model uncertainty.
In this paper, a project is described which is a 2-D
modelled version of a car that will learn how to drive itself. It
will have to figure everything out on its own. In addition, to
achieve that the simulator contains a car running
simultaneously &can be controlled by different control
algorithms - heuristic, reinforcement learning-based, etc. For
each dynamic input, the Reinforcement- Learning modifies
new patterns. Ultimately, Reinforcement Learning helps in
maximizing the reward from every state. In this first Part, we
will implement a Reinforcement-Learning model to build an
AI for Self Driving Car. Project will be focusing on the brain
of the car not any graphics. The car will detect obstacles and
take basic actions. To make autonomous car or self-driving
car a reality, some of the factors to be considered are human
safety and quality of life.
IDENTIFICATION OF RANGE OF THRESHOLDS FOR FUZZY INPUTS IN TRAFFIC FLOW CIV8331Umar Ali
Identifying range of thresholds for fuzzy inputs in traffic flow. Fuzzy logic is used in identifying fuzzy sets that are used traffic behaviours. Fuzzy logic can be applied in the development of automated traffic control systems, adaptive cruise control and self driving vehicles.
Motorcycle Movement Model Based on Markov Chain Process in Mixed TrafficIJECEIAES
Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4 order polynomial.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
Shared Steering Control between a Driver and an Automation: Stability in the ...paperpublications3
Abstract: Now-a-days the Automatic control has been increasingly implemented for vehicle control system. Especially the steering control is essential for preventing accidents. In the existing systems there is no fully automatic steering control and it has serious problems. When it is made automatic, the system complexity is more. So, the shared steering concept is used in the proposed system to avoid accidents. In this, the position of the road is found using the web camera installed in front of the vehicle which is connected to the PC installed with MATLAB. Using MATLAB the image is processed to check the road characteristics. This paper presents an advanced driver assistance system (ADAS) for lane keeping, together with an analysis of its performance and stability with respect to variations in driver behavior. The automotive ADAS proposed is designed to share control of the steering wheel with the driver in the best possible way. Its development was derived from an H2-Preview optimization control problem, which is based on a global driver–vehicle–road (DVR) system. The DVR model makes use of a cybernetic driver model to take into account any driver–vehicle interactions. Such a formulation allows 1) Considering driver assistance cooperation criteria in the control synthesis, 2) improving the performance of the assistance as a cooperative copilot, and 3) analyzing the stability of the whole system in the presence of driver model uncertainty. The developed assistance system improved lane-keeping performance and reduced the risk of a lane departure accident. Good results were obtained using several criteria for human–machine cooperation. Poor stability situations were successfully avoided due to the robustness of the whole system, in spite of a large range of driver model uncertainty.
In this paper, a project is described which is a 2-D
modelled version of a car that will learn how to drive itself. It
will have to figure everything out on its own. In addition, to
achieve that the simulator contains a car running
simultaneously &can be controlled by different control
algorithms - heuristic, reinforcement learning-based, etc. For
each dynamic input, the Reinforcement- Learning modifies
new patterns. Ultimately, Reinforcement Learning helps in
maximizing the reward from every state. In this first Part, we
will implement a Reinforcement-Learning model to build an
AI for Self Driving Car. Project will be focusing on the brain
of the car not any graphics. The car will detect obstacles and
take basic actions. To make autonomous car or self-driving
car a reality, some of the factors to be considered are human
safety and quality of life.
IDENTIFICATION OF RANGE OF THRESHOLDS FOR FUZZY INPUTS IN TRAFFIC FLOW CIV8331Umar Ali
Identifying range of thresholds for fuzzy inputs in traffic flow. Fuzzy logic is used in identifying fuzzy sets that are used traffic behaviours. Fuzzy logic can be applied in the development of automated traffic control systems, adaptive cruise control and self driving vehicles.
Motorcycle Movement Model Based on Markov Chain Process in Mixed TrafficIJECEIAES
Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4 order polynomial.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
1. BAYERO UNIVERSITY KANO.
DEPARTMENT OF CIVIL ENGINEERING
ADVANCE TRAFFIC ENGINEERING CIV8331
ASSIGNMENT
REVIEW ON FUZZY MICROSCOPIC TRAFFIC FLOW
MODEL
UMAR MAGAJI YARIMA
SPS/17/MCE/00033
umarmyarima@gmail.com
Course lecturer : Prof. Hashim M. Alhassan
MAY, 2018
2. INTRODUCTION
FUZZY LOGIC
Fuzzy logic: is a form of much value logic in which the truth
values of variables may be any real number between 0 and 1. It is
employed to handle the concept of partial truth, where the truth
value may range between completely true and completely false
(Wikipedia).
Lotfi Zadeh is credited with fuzzy logic’s formulation, which he
developed while working for the University of California in the
1960s. Zadeh’s research focused on implementing methods that
allowed computers to understand human language, such as
multiple degrees of attributes that cannot be described in terms
of zeroes and ones (Techopedia)
3. In order that computers achieve human capability in
solving problems, computers most be able to solve
problems for which only incomplete and/or imprecise
information is available. And fuzzy logic is the tool
available for making computer to be able to solve
problems using imprecise information.
Example: let consider speed in traditional logic and in
fuzzy logic.
If speed = 0 then the car is slow
If speed = 1 then the car is fast
4. Fuzzy logic is now available to solve the problem.
If speed is between 0 – 0.25 then the car is slowest
If speed is between 0.25 – 0.50 then the car is slow
If speed is between 0.50 – 0.75 then the car is fast
If speed is between 0.75 – 1.0 then the car is fastest
where slowest, slow, fast and fastest are called fuzzy sets.
7. FUZZY LOGIC CAR FOLLOWING
MODEL
The fuzzy logic car-following model describes driving
operations under car-following conditions using
linguistic terms and associated rules, instead of
deterministic mathematical functions. Car-following
behaviour can be described in a natural manner that
reflects the imprecise and incomplete sensory data
presented by human sensory modalities. The fuzzy
logic car-following model treats a driver as a
decision-maker who decides the controls based
on sensory inputs using a fuzzy reasoning.
8. STATEMENT OF THE PROBLEM
Most of the cars following models have established a
unique interpretation of drivers’ car-following behaviour
without considering how the drivers behave in reality.
And most of the assumptions in the models were used to
develop automatic systems that control driving speeds
and headway distances while following a vehicle, such as
adaptive cruise control systems, The automatic vehicle
control system maintains a safe headway distance while
following a vehicle and controls velocity according to the
relative speed of the leading vehicle, in order to avoid a
rear-end collision.
9. If the system’s automatic controls do not match the
driver’s manual controls, driver acceptance of the
automatic vehicle control systems decreases, and the
driver is not likely to use them. For example, when a lead
vehicle speeds up and the inter-vehicle distance
increases, one driver may accelerate strongly, whereas
another driver may accelerate slightly; and other drivers
may not accelerate. The system’s automatic hard
acceleration does not suit drivers whose acceleration is
slight and those who do not accelerate, and they may
regard such automatic systems as dangerous. Therefore,
it is expected that drivers will accept longitudinal control
systems that operate in a manner similar to their own
usual car- following behaviour.
10. AIM AND OBJECTIVES
AIM:
To understand fuzzy microscopic car following model
OBJECTIVES:
To determine the degree to which a driver controls
longitudinal acceleration according to the relationship
between the preceding vehicle and his/her vehicle.
To evaluates the driver’s acceleration and deceleration
rates using a rule base in natural language.
11. LITERATURE REVIEW
review of car-following models
Car-following models have been developed since the
1950s (e.g., Pipes, 1953). Many models describe the
accelerative behaviour of a driver as a function of inter-
vehicle separation and relative speed. The following are
representative car-following models.
General Motors Model
The fundamental concept behind the General Motors
Model is the stimulus-response theory (Chandler et al.,
1958).
Response = Stimulus × Sensitivity
12. Stopping-Distance Model: The Stopping-Distance
Model assumes that a following vehicle always maintains
a safe following distance in order to bring the vehicle to a
safe stop if the leading vehicle suddenly stops. This
model is based on a function of the speeds of the
following and leading vehicles and the follower’s
reaction time. The original formulation (Kometani, &
Sasaki, 1959)
The Stopping-Distance Model is widely used in
microscopic traffic simulations (Gipps, 1981), because of
its easy calibration based on a realistic driving behaviour,
requiring only the maximal deceleration of the following
vehicle. However, the “safe headway” concept is not a
totally valid starting point, and this assumption is not
consistent with empirical observations.
13. Action-Point Model:
The Action-Point Model is the first car-following model
to incorporate human perception of motion. The model
developed by Michaels suggests that the dominant
perceptual factor is changes in the apparent size of the
vehicle (i.e., the changing rate of visual angle) (Michaels,
1963).
This model assumes that a driver appropriately
accelerates or -decelerates if the angular velocity exceeds
a certain threshold. Once the threshold is exceeded, the
driver chooses to decelerate until he/she can no longer
perceive any relative velocity. Thresholds include a
spacing-based threshold that is particularly relevant in
close headway situations, a relative speed threshold for
the perception of closing,
14. and thresholds for the perception of opening and closing for low
relative speeds (a recent work suggests that the perception of
opening and that of closing have different thresholds (Reiter,
1994)). Car-following conditions are further categorized into
subgroups: free driving, overtaking, following, and emergency
situation. A driver engages in different acceleration behaviours in
different situations when the perceived physical perception
exceeds the thresholds.
The Action-Point Model takes into account the human threshold
of perception, establishing a realistic rationale. However, various
efforts have focused on identifying threshold values during the
calibration phase, while the adjustment of acceleration above the
threshold has not been considered, and the acceleration rate is
normally assumed to be a constant. Additionally, the model
dynamic (switching between the subgroups) has not been
investigated. Finally, the ability to perceive speed differences and
estimate distances varies widely among drivers. Therefore, it is
difficult to estimate and calibrate the individual thresholds
associated with the Action-Point Model.
15. Fuzzy logic car-following model
Drivers perform a car-following task with real-time
information processing of several kinds of information
sources. The car-following models discussed above have
established a unique interpretation of drivers’ car-
following behaviours. A driver in a car-following
situation is described as a stimuli-responder in the
General Motors Model, a safe distance- keeper in the
Stopping-Distance Model, and a state monitor who
wants to keep perceptions below the threshold in the
Action-Point Model. However, these models include
non-realistic constraints to describe car-following
behaviour in real road-traffic environments: symmetry
between acceleration and deceleration, the “safe
headway” concept, and constant acceleration or
deceleration above the threshold.
16. The fuzzy logic car-following model describes driving
operations under car-following conditions using
linguistic terms and associated rules, instead of
deterministic mathematical functions. Car-following
behaviour can be described in a natural manner that
reflects the imprecise and incomplete sensory data
presented by human sensory modalities. The fuzzy logic
car-following model treats a driver as a decision-maker
who decides the controls based on sensory inputs using a
fuzzy reasoning. There are two types of fuzzy inference
system that uses fuzzy reasoning to map an input space
to an output space, Mandani-type and Sugeno-type. The
main difference between the Mamdani and Sugeno types
is that the output membership functions are only linear
or constant for Sugeno-type fuzzy inference.
17. Case study :Longitudinal study of elderly drivers’ car-following
behaviour using fuzzy logic
This section introduces a case study focusing on the assessment
of elderly drivers’ car- following behaviour, using the proposed
fuzzy logic car-following model. The number of elderly drivers
who drive their own passenger vehicles in their daily lives has
increased annually. Driving a vehicle expands everyday activities
and enriches the quality of life for the elderly. However, cognitive
and physical functional changes of elderly drivers may lead to
their increased involvement in traffic accidents. Thus, it is
important to develop advanced driver assistance and support
systems that promote safe driving for elderly drivers. Automatic
vehicle control systems are expected to enhance comfort as well
as safety when elderly drivers follow a vehicle. Understanding
elderly drivers’ car-following behaviour is essential for
developing automatic control systems that adapt to their usual
car-following behaviour.
18. PROCEDURE
Field experiments were conducted in 2003 (first
experiment) and in 2008 (second experiment) on rural
roads around Tsukuba(Japan). The two experiments
were conducted using the same instrumented vehicle,
the same driving route, and the same participants. The
AIST instrumented vehicle was used for the behavioural
data collection (Brackstone et al., 1999; Sato &
Akamatsu, 2007). Almost all the sensors and the driving
recorder system were fixed inside the trunk, so that the
participating drivers could not see them, in order to
encourage natural driving behaviours during the
experiment trials.
19. Four elderly drivers (three males and one female) with
informed consent participated in the two experiments.
Their ages ranged from 65 to 70 years (average 67.3
years) in the first experiment and from 70 to 74 years
(average 72.0 years) in the second experiment. Their
annual distance driven ranged from 5,000 to 8,000km in
the first experiment and from 2,000 to 10,000km in the
second experiment (average 6,500km in both
experiments).
The participants were instructed to drive in their typical
manner. In the first experiment, the recorded trip for
each elderly participant was made once a day on
weekdays, for a total of 10 trips. In the second
experiment, the trial was conducted twice a day on
weekdays, for a total of 30 trips. The participants took a
break between the experiment trials in the second
experiment.
20. RESULT
Figure 6, compares the relative velocity- acceleration
mapping of the first and second experiments.
The deceleration when the elderly participants
approach the lead vehicle was the same in the two
experiments. However, the elderly drivers accelerate
more strongly in the second experiment than in the
first experiment, when the leading vehicle goes faster
and the headway distance is opening.
22. Figure 7, Comparison of THW to leading and
following vehicles between the first and second
experiments.
The peak of the distribution of THW to leading
vehicles is found in the category from 1 to 1.5s in the
first experiment. However, the peak is found in the
category from 1.5 to 2s in the second experiment,
indicating that the THW in the second experiment
exceeds that in the first experiment.
24. DISCUSSION
Comparison of THW to following vehicles between
the first and second experiments indicates no change
in traffic flow on the target section in five years. In
contrast, THW to leading vehicles is longer in the
second experiment than in the first experiment,
suggesting that elderly drivers take longer THW and
the static aspect of their car-following behaviours
changes over five years.
25. The task-capability interface model (Fuller, 2005)
helps clarify why elderly drivers’ car- following
behaviour changes with aging. In this model, drivers
adjust task difficulty while driving in order to avoid
road accidents. Task difficulty can be described as an
interaction between the driver’s capability and task
demands. When the driver’s capability exceeds the
task demands, the task is easy and the driver
completes the task successfully. When the task
demands exceed the driver’s capability, the task is
difficult and a collision or loss of control occurs
because the driver fails to accomplish the task.
Therefore, elderly drivers reduce task demands by
adopting longer THW to a leading vehicle.
26. The results of the fuzzy logic car-following model
estimation suggest that the acceleration rate when the
inter-vehicle distance is opening becomes higher after
five years, although the deceleration rate while
approaching the vehicle in front does not change. The
stronger acceleration may be a compensating
behaviour for maintaining the driver’s capability by
increasing the task demand temporarily, because the
driver’s capability interacts with the task demands,
and drivers can control the task demands by changing
their driving behaviour in order to improve their
capability (e.g., increasing speed, to wake up when
feeling sleepy while driving).
27. CURRENT RESEARCH IN THE AREA
A study to examine individual differences in car-
following behaviours to clarify which cognitive function
influences changes in car-following behaviour with
aging, to assess the relationship between car-following
behaviour on a real road and elderly drivers’ cognitive
functions (e.g., attention, working memory, and
planning (Kitajima & Toyota, 2012)) measured in a
laboratory experiment. Analysis of the relationship
between driving behaviour and a driver’s cognitive
functions will help determine how driver support
systems may assist driving behaviour and detect the
driver’s cognitive functions based on natural driving
behaviour.
28. PROBLEMS OF RESEARCH IN THE
AREA
The fuzzy logic car-following model deals mainly with two
vehicles: a vehicle in front and the driver’s own vehicle. This
model can determine the degree to which a driver controls
longitudinal acceleration according to the relationship between
the preceding vehicle and his/her vehicle.
When drivers are approaching a traffic light under car-following
conditions, they may pay more attention to the signal in front of
the leading vehicle and manage their acceleration based on the
traffic light.
When drivers are approaching tight curve, drivers allocate their
attention to the forward road structure instead of the leading
vehicle.
The car-following behaviour can be influenced by environmental
factors other than a lead vehicle.
29. FUTURE DIRICTIONS IN THE
SUBJECT AREA
Further fuzzy car-following models should be develop
to reproduce the car-following behaviour when:
Drivers are approaching intersection.
Drivers are approaching tight curve.
When drivers are under some environmental
conditions
30. REFERENCES
Available from https://www.investopedia.com/terms/f/fuzzy-
logic.asp
Available from http://www.techopedia.com/definition/1809/fuzzy-
logic
Available from http://en.m.wikipedia.org/wiki/fuzzy_logic,Last
edited on 1 May 2018 at 14:31
Harcourt,H.M.,(2010),Webster’s new world collage dictionary, 4th
edition
Toshihisa Sato and Motoyuki Akamatsu (2012). Understanding
Driver Car-Following Behavior Using a Fuzzy Logic Car-Following
Model, Fuzzy Logic - Algorithms, Techniques and
Implementations, Prof. Elmer Dadios (Ed.), ISBN: 978-953-51-0393-
6, InTech, Available from:
http://www.intechopen.com/books/fuzzy-logic- algorithms-
techniques-and-implementations/understanding-driver-car-
following-behavior-using-fuzzy-logic-car- following-model
