Abstract: (22200 Views)
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.