Showing 2 results for Intelligent Transportation Systems
A. Ghaffari, A. Khodayari, S. Arvin, F. Alimardani,
Volume 2, Issue 4 (10-2012)
Abstract
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. Khodayari,
Volume 5, Issue 2 (6-2015)
Abstract
Due to the increasing demand for traveling in public transportation systems and increasing traffic of vehicles, nowadays vehicles are getting to be intelligent to increase safety, reduce the probability of accident and also financial costs. Therefore, today, most vehicles are equipped with multiple safety control and vehicle navigation systems. In the process of developing such systems, simulation has become a cost-effective chance for the fast evolution of computational modeling techniques. The most popular microscopic traffic flow model is car following models which are increasingly being used by transportation experts to evaluate new Intelligent Transportation System (ITS) applications. The control of car following is essential to its safety and its operational efficiency. This paper presents a car-following control system that was developed using a fuzzy model predictive control (FMPC). This system was used to simulate and predict the future behavior of a Driver-Vehicle-Unit (DVU) and was developed based on a new idea to calculate and estimate the instantaneous reaction of a DVU. At the end, for experimental evaluation, the FMPC system was used along with a human driver in a driving simulator. The results showed that the FMPC has better performance in keeping the safe distance in comparison with real data of human drivers behaviors. The proposed model can be recruited in driver assistant devices, safe distance keeping observers, collision prevention systems and other ITS applications.
