MANFIS-based overtaking maneuver modeling and prediction of a Driver-Vehicle-Unit in real traffic flow

The purpose of this study is to design multiple-input multiple-output ANFIS (MANFIS) models to simulate and predict the future state of the overtaking maneuver in real traffic flow for four different time steps ahead. These models are designed to predict the behavior for 1, 2, 4 and 6 time steps ahead. Each time step is equal to 0.1 second. In these models, important factors such as distance, velocity, acceleration and the movement angle of the overtaking vehicle are considered. In these models, for all the variables, instantaneous values are used and none of them is considered constant. The presented models predict the future value of the acceleration and the movement angle of the overtaking vehicle. These models are designed based on the real traffic data and validated at the microscopic level. The results show very close agreement between field data and models outputs. The proposed models can be employed ITS applications and the like.

[1]  Ghaffari,et al.  Using the Reaction Delay as the Driver Effects in the Development of Car-Following Models , 2012 .

[2]  Ali Ghaffari,et al.  ANFIS Based Modeling and Prediction Lane Change Behavior in Real Traffic Flow , 2011 .

[3]  M. Treiber,et al.  Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data , 2008, 0804.0108.

[4]  Xiwei Guo,et al.  Influences of overtaking on two-lane traffic with signals , 2010 .

[5]  Sitti Asmah Hassan,et al.  Driver's overtaking behavior on single carriageway road , 2005 .

[6]  E. R. Cohen An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements , 1998 .

[7]  Ali Ghaffari,et al.  An ANFIS design for prediction of future state of a vehicle in lane change behavior , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[8]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[9]  Napsiah Ismail,et al.  Cutting parameters identification using multi adaptive network based Fuzzy inference system: An artificial intelligence approach , 2011 .

[10]  A. Khodayari,et al.  Overtaking Maneuver Behaviour Modeling Based on Adaptive Neuro-Fuzzy Inference System , 2011 .

[11]  Tzila Shamir,et al.  How should an autonomous vehicle overtake a slower moving vehicle: design and analysis of an optimal trajectory , 2004, IEEE Transactions on Automatic Control.

[12]  Ming Yang,et al.  Conflict-Probability-Estimation-Based Overtaking for Intelligent Vehicles , 2009, IEEE Transactions on Intelligent Transportation Systems.

[13]  Ali Ghaffari,et al.  ANFIS based modeling for overtaking maneuver trajectory in motorcycles and autos , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[14]  Hai-Jun Huang,et al.  A new overtaking model and numerical tests , 2007 .

[15]  José Eugenio Naranjo,et al.  Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver , 2008, IEEE Transactions on Intelligent Transportation Systems.

[16]  M. Treiber,et al.  Estimating Acceleration and Lane-Changing Dynamics Based on NGSIM Trajectory Data , 2007 .

[17]  Ali Ghaffari,et al.  A Modified Car-Following Model Based on a Neural Network Model of the Human Driver Effects , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  Moshe Livneh,et al.  Evaluation of the Passing Process on Two-Lane Rural Highways , 2000 .

[19]  Tharwat O. S. Hanafy A modified Algorithm to Model Highly Nonlinear System , 2010 .