Showing 2 results for Automation
A. Ghaffari, A. Khodayari, F. Alimardani, H. Sadati,
Volume 3, Issue 2 (6-2013)
Abstract
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.
Dr Javad Sharifi, Ms Fereshte Vaezi,
Volume 9, Issue 2 (6-2019)
Abstract
Modeling and identification of the system of Iranian cars is one of the most basic needs of automotive and consumer groups and has a broad role for safe driving. It has happened with speed increasing or changing of shift gear, effects on water temperature or the car's torque has been observed, but how much and how intensely and with what algorithm this effect is identifiable, can be modeled and controlled, because up to now an algorithm that can show these effects during driving has not existed that what reaction should be made by the vehicle when it occurs untimely.
Identification of each automobile sector lonely has been considered in recent decades, and in some cases, some relationships have been investigated, but from a control point of view, the lack of comprehensive effects of all parts of a car on the other parts is to get an identification algorithm in the automotive industry, and it requires more in-depth studies, because the complexity of the behavior of different parts of the car has made many attempts not fully understandable. Hear it's supposed to control different parameters of Iranian vehicles by using LS estimation and fuzzy logic controller and the simulation is done in Matlab software by storing and validating data of a Dena vehicle through CAN network.
