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.
