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Showing 9 results for Ghaffari

A. Khodayari, A. Ghaffari,
Volume 2, Issue 1 (1-2012)

Car-following models, as the most popular microscopic traffic flow modeling, is increasingly being used by transportation experts to evaluate new Intelligent Transportation System (ITS) applications. A number of factors including individual differences of age, gender, and risk-taking behavior, have been found to influence car-following behavior. This paper presents a novel idea to calculate the Driver-Vehicle Unit (DVU) instantaneous reaction delay of DVU as the human effects. Unlike previous works, where the reaction delay is considered to be fixed, considering the proposed idea, three input-output models are developed to estimate FV acceleration based on soft computing approaches. The models are developed based on the reaction delay as an input. In these modeling, the inputs and outputs are chosen with respect to this feature to design the soft computing models. The performance of models is evaluated based on field data and compared to a number of existing car-following models. The results show that new soft computing models based on instantaneous reaction delay outperformed the other car-following models. The proposed models can be recruited in driver assistant devices, safe distance keeping observers, collision prevention systems and other ITS applications.
A. Ghaffari, A. Khodayari, S. Arvin, F. Alimardani,
Volume 2, Issue 4 (10-2012)

The lane change maneuver is among the most popular driving behaviors. It is also the basic element of important maneuvers like overtaking maneuver. Therefore, it is chosen as the focus of this study and novel multi-input multi-output adaptive neuro-fuzzy inference system models (MANFIS) are proposed for this behavior. These models are able to simulate and predict the future behavior of a Driver-Vehicle-Unit in the lane change maneuver for various time delays. To design these models, the lane change maneuvers are extracted from the real traffic datasets. But, before extracting these maneuvers, several conditions are defined which assure the extraction of only those lane change maneuvers that have a smooth and uniform trajectory. Using the field data, the outputs of the MANFIS models are validated and compared with the real traffic data. In addition, the result of these models is compared with the result of other trajectory models. This comparison provides a better chance to analyze the performance of these models. The simulation results show that these models have a very close compatibility with the field data and reflect the situation of the traffic flow in a more realistic way.
A. Ghaffari, A. Khodayari, F. Alimardani, H. Sadati,
Volume 3, Issue 2 (6-2013)

Overtaking a slow lead vehicle is a complex maneuver because of the variety of overtaking conditions and driver behavior. In this study, two novel prediction models for overtaking behavior are proposed. These models are derived based on multi-input multi-output adaptive neuro-fuzzy inference system (MANFIS). They are validated at microscopic level and are able to simulate and predict the future behavior of the overtaking vehicle in real traffic flow. In these models, the kinematic features of Driver-Vehicle-Units (DVUs) such as distance, velocity, and acceleration are used. Unlike the previous models, where some variables of the two involved vehicles are considered to be constant, in this paper, instantaneous values of the variables are considered. The first model predicts the future value of the longitudinal acceleration and the movement angle of the overtaking vehicle. The other model predicts the overtaking trajectory for the overtaking vehicle. The second model is designed for two different vehicle classes: motorcycles and autos. Also, the result of the trajectory prediction model is compared with the result of other models. This comparison provides a better chance to analyze the performance of this model. Using the field data, the outputs of the MANFIS models are validated and compared with the real traffic dataset. The simulation results show that these two MANFIS models have a very close compatibility with the field data and reflect the situation of the traffic flow in a more realistic way. These models can be used for all types of drivers and vehicles and also in other roads and are not limited to certain types of situations. The proposed models can be employed in ITS applications and the like.
A. Ghaffari , A. Khodayari , B. Gharehpapagh , S. Salehinia ,
Volume 4, Issue 2 (6-2014)

In this paper a control system has been designed to improve traffic conditions in car following maneuver. There are different methods to design a control system. In this paper design approach is based on the Fuzzy sliding mode control (FSMC) system. The aim of designing FSMC system is to achieve safe and desire longitudinal distance and less lateral displacement. In order to control and obtain desired longitudinal and lateral movements, suitable values of composite torque and steering angle is generated. At first to design of FSMC system, a nonlinear dynamics model of vehicle with three degrees of freedom is presented and validated with real traffic data. Then, the performance of the FSMC system has been evaluated by real car following data. At the end, the simulation results of FSMC are compared with the first and second order sliding mode control. Simulation result shows that performance of FSMC is better than sliding mode control. Also by comparing between FSMC and real driver, it is shown that FSMC is much safer than a real human driver in keeping the longitudinal distance and also the FSMC produces less lateral displacement in the lateral movement too.
A. Ghaffari, A.r. Khodayari, S. Arefnezhad,
Volume 6, Issue 4 (12-2016)

The designing of advanced driver assistance systems and autonomous vehicles needs measurement of dynamical variations of vehicle, such as acceleration, velocity and yaw rate. Designed adaptive controllers to control lateral and longitudinal vehicle dynamics are based on the measured variables. Inertial MEMS-based sensors have some benefits including low price and low consumption that make them suitable choices to use in vehicle navigation problems. However, these sensors have some deterministic and stochastic error sources. These errors could diverge sensor outputs from the real values. Therefore, calibration of the inertial sensors is one of the most important processes that should be done in order to have the exact model of dynamical behaviors of the vehicle. In this paper, a new method, based on artificial neural network, is presented for the calibration of an inertial accelerometer applied in the vehicle navigation. Levenberg-Marquardt algorithm is used to train the designed neural network. This method has been tested in real driving scenarios and results show that the presented method reduces the root mean square error of the measured acceleration up to 96%. The presented method can be used in managing the traffic flow and designing collision avoidance systems.

A. Khodayari, A. Ghaffari, F. Fanni,
Volume 7, Issue 1 (3-2017)

Advanced Driver Assistance Systems (ADAS) benefit from current infrastructure to discern environmental information. Traffic signs are global guidelines which inform drivers from near characteristics of paths ahead. Traffic Sign Recognition (TSR) system is an ADAS that recognize traffic signs in images captured from road and show information as an adviser or transmit them to other ADASs. In this paper presents a novel machine vision algorithm for traffic sign recognition based on fuzzy sets. This algorithm is a pipeline consists of multiple fuzzy set that create a fuzzy space here called Super Fuzzy Set (SFS). SFS helped to design a flexible and fast algorithm for recognizing traffic signs in a real-time application. Designed algorithm was implemented in computer-based system and checked on a test car in real urban environment. 83.34% accuracy rate was obtained in real-time test.

M.r. Emami Shaker , A. Ghaffari, A. Maghsoodpour, A. Khodayari,
Volume 7, Issue 4 (12-2017)

The Global Positioning System (GPS) and an Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characters in many aspects, a GPS/INS integrated navigation system has been a hot research topic in the recent decade. The Micro Electrical Mechanical Sensors (MEMS) successfully solved the problems of price, size and weight with the traditional INS. Therefore they are commonly applied in GPS/INS integrated systems. The biggest problem of MEMS is the large sensor errors, which rapidly degrade the navigation performance in an exponential speed. Three levels of GPS/IMU integration structures, i.e. loose, tight and ultra tight GPS/IMU navigation, are proposed by researchers. The loose integration principles are given with detailed equations as well as the basic INS navigation principles. The Extended Kalman Filter (EKF) is introduced as the basic data fusion algorithm, which is also the core of the whole navigation system to be presented. The kinematic constraints of land vehicle navigation, i.e. velocity constraint and height constraint, are presented. A detailed implementation process of the GPS/IMU integration system is given. Based on the system model, we show the propagation of position standard errors with the tight integration structure under different scenarios. A real test with loose integration structure is carried out, and the EKF performances as well as the physical constraints are analyzed in detail.
Mr. Mohammad Yar-Ahmadi, Mr. Hamid Rahmanei, Prof. Ali Ghaffari,
Volume 13, Issue 1 (3-2023)

The primary purpose of each autonomous exit parking system is to facilitate the process of exiting the vehicle, emphasizing the comfort and safety of driving in the absence of almost any human effort. In this paper, the problem of exit parking for autonomous vehicles is addressed. A nonlinear kinematic model is presented based on the geometric relationship of the vehicle velocities, and a linear time-varying discrete-time model of the vehicle is obtained for utilizing the optimal control strategy. The proposed path planning algorithm is based on the minimization of a geometric cost function. This algorithm works for ample space exit parking in Single-Maneuver and tight spaces in Multi-Maneuver exit parking. Finally, an optimal discrete-time linear quadratic control approach is hired to minimize a quadratic cost function. To evaluate the performance of the proposed algorithm, the control system is simulated by MATLAB/Simulink software. The results show that the optimal control strategy is well able to design and follow the desired path in each of the exit parking maneuvers.

Mr. Hamid Rahmanei, Dr. Abbas Aliabadi, Prof. Ali Ghaffari, Prof. Shahram Azadi,
Volume 13, Issue 2 (6-2023)

The coordinated control of autonomous electric vehicles with in-wheel motors is classified as over-actuated control problems requiring a precise control allocation strategy. This paper addresses the trajectory tracking problem of autonomous electric vehicles equipped with four independent in-wheel motors and active front steering. Unlike other available methods presenting optimization formulation to handle the redundancy, in this paper, the constraints have been applied directly using the kinematic relations of each wheel. Four separate sliding mode controllers are designed in such a way that they ensure the convergence of tracking errors, in addition to incorporating the parametric and modeling uncertainties. The lateral controller is also designed to determine the front steering angles to eliminate lateral tracking errors. To appraise the performance of the proposed control strategy, a co-simulation is carried out in MATLAB/Simulink and Carsim software. The results show that the proposed control strategy has enabled the vehicle to follow the reference path and has converged the errors of longitudinal and lateral positions, velocity, heading angle, and yaw rate. Furthermore, the proposed control system shows promising results in the presence of uncertainties including the mass and moment of inertia, friction coefficient, and the wind disturbances.

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