DEVELOPMENT OF A REALISTIC DRIVING BEHAVIOR BY MEANS OF FUZZY INFERENCE SYSTEM
DOI:
https://doi.org/10.11113/jt.v74.4836Keywords:
Microscopic traffic flow model, intelligent vehicle, Fuzzy Inference System (FIS), realistic decision moduleAbstract
Realistic traffic flow simulation is possible when the vehicles inside the simulation are able to mimic human driving behavior. In view of this, this paper will discuss the implementation of fuzzy logic inside the Behavior Model framework with the intention to develop intelligent simulated vehicles. This Behavior Model consists of three different units, namely; Vision and Perception, Decision and Motion Control Unit. Vision and Perception Unit acts as the eyes for the intelligent vehicle. Decision Unit will decide the maneuvering decision. Finally, Motion Control Unit will transfer the decision into motion. However, the implementation of fuzzy logic with the integration of fuzzy rules and defuzzification techniques is done in the first and second units. This Behavior Model is controlled by two sets of fuzzy inference systems (FIS) which are free flow vehicles following and changing lanes. The finding of this research shows that the Behavior Model with fuzzy logic is able to create an intelligent vehicle that is able to self-maneuveri inside the traffic flows, realistically.Â
References
Al-Shihabi, T., and R. R. Mourant. 2001. A Framework for Modeling Human-like Driving Behaviors For Autonomous Vehicles in Driving Simulators. In Proceedings of the 5th International Conference On Autonomous Agents. Montreal, Canada. 286-291.
Al-Shihabi, T., and R. R. Mourant. 2003. Toward More Realistic Driving Behavior Models for Autonomous Vehicles in Driving Simulators. Journal of the Transportation Research Board. 1843: 41-43.
Yu, Y., A. E. Kamel, and G. Gong. 2013. Modeling Intelligent Vehicle Agent in Virtual Reality Traffic Simulation System. 2nd international Conference on Systems and Computer Science (ICSCS 2013). 274-279.
Punzo, V. and B. Ciuffo. 2011. Integration of Driving and Traffic Simulation: Issues and First Solutions. IEEE Transaction on Intelligent Transportation System. 12(2): 354-363.
Khodayari, A., A. Ghaffari, R. Kazemi, and N. Manavizadeh. 2010. ANFIS Based Modeling and Prediction Car Following Behavior in Real Traffic Flow Based on Instantaneous Reaction Delay. 13th International IEEE Conference on Intelligent Transportation Systems (ITSC 2010), Portugal. 599-604.
Zadeh, L. A. 1965. Fuzzy Sets. Information and Control. 8: 338-353.
Khodayari, A., R. Kazemi, A. Ghaffari, and N. Manavizadeh. 2010. Modeling and Intelligent Control Design of Car Following Behavior in Real Traffic Flow. IEEE International Conference on Cybernetics and Intelligent Systems (CIS2010). Singapore. 261-266.
Zheng, P. and M. McDonald. 2005. Application of Fuzzy Systems in the Car Following Behaviour Analysis. Fuzzy Systems and Knowledge Discovery. Lecture Notes in Computer Science. 3613: 782-791.
Khodayari, A., R. Kazemi, and A. Ghaffari. 2011. Design of An Improved Fuzzy Logic Based Model for Prediction of Car Following Behavior. In Proc. 2011 IEEE International Conference on Mechatronics. Istanbul, Turkey.
Treiber, M. 2008. Micro-simulation of Road Traffic. Institute for Transports and Economics, Dresden University of Technology, http://www.tra_c-simulation.de.
Deibel, K. 2007. Traffic Light Simulations. Computer Science & Engineering, University of Washington, http://www.cs.washington.edu/homes/deibel/rt/.
Hoogendoorn, S., S. Hoogendoorn–Lanser, and H. Schuurman. 1998. Fuzzy perspectives in traffic engineering. Research Report, TRAIL Research School, Delft, report on behalf of Dutch Ministry of Transport.
Chattaraj, U. and M. Panda. 2010. Some Applications of Fuzzy Logic in Transportation Engineering. Proceedings of International Conference on Challenges and Applications of Mathematics in Science and Technology (CAMIST). NIT Rourkela.
Dell’Orco, M. and M. Ottomanelli. 2012. Simulation of Users Decision in Transport Mode Choice Using Neuro-Fuzzy Approach. Computational Science and Its Applications, Lecture Notes in Computer Science. 7334: 44-53.
Milanés, V., J. Pérez, J. Godoy, and E. Onieva. 2012. A Fuzzy Aid Rear-end Collision Warning/Avoidance System. Expert System With Applications. 39(10): 9097-9107.
Valdés, Vela, M. R. Toledo Moreo, F. Terroso Sáenz, and M. Zamora-Izquierdo. 2013. An Application of a Fuzzy Classifier Extracted from Data For Collision Avoidance Support In Road Vehicles. Engineering Applications of Artificial Intelligence. 26(1): 173-183.
McDonald M., Brackstone M., & Wu, J. 1997. Development of a Fuzzy Logic Based Microscopic Motorway Simulation Model. In Proceedings of the IEEE Conference o Intelligent Transportation Systems (ITSC97). Boston, USA,
J. Wu, M. Brackstone, M. McDonald. 2000. Fuzzy Sets and Systems for a Motorway Microscopic Simulation Model. Fuzzy Sets Syst. 116(1): 65-76.
Hautière, N., R. Labayrade, and D. Aubert. 2006. Real-Time Disparity Contrast Combination for Onboard Estimation of the Visibility Distance. IEEE Transaction of Intelligent Transportation Systems. 7(2): 201-212.
Hautière, N., J. P. Tarel and D. Aubert. 2010. Mitigation of Visibility Loss for Advanced Camera-Based Driver Assistance. IEEE Transaction of Intelligent Transportation System. 11(2): 474-484.
HMSO. 1993. The Highway Code (London, UK: HMSO).
Official Portal of Road Transport in Malaysia. 2015. http://www.jpj.gov.my/web/eng.
Nilsson, R. 2000. Drivers’ Impressions of Front and Rear Gaps in Queues. Ergonomics. 43(12): 1985-2000.
Evans, L. and R. Rothery. 1976. The Influence of Forward Vision and Target Size on Apparent Inter-Vehicular Spacing. Transportation Science. 10(1): 85-101.
Broughton, K. L. M., F. Switzer, and D. Scott. 2007. Car Following Decisions Under Three Visibility Conditions and Two Speeds Tested With a Driving Simulator. Accident Analysis and Prevention. 39(1): 106-116.
Herman, R. and Potts, R. B. 1959. Single Lane Traffic Theory and Experiment. In Proceedings of the Symposium on Theory of Traffic Flow, Research Labs, General Motors. New York: Elsevier. 147-157.
G. H. Bham and R. F. Benekohal. 2004. A High Fidelity Traffic Simulation Model Based on Cellular Automata and Car-Following Concepts. Transportation Research, Part C: Emergency Technology. 12(1): 1-32.
M. Aycin and R. Benekohal. 1998. Linear Acceleration Car-Following Model Development and Validation. Transportation Research Record. 1644: 10-19.
Naranjo, J. E., C. Gonz´alez, J. Reviejo, R. Garc´ıa, T. de Pedro, and M. A. Sotelo. 2007. Using Fuzzy Logic in Automated Vehicle Control. IEEE Intelligent System. 22(1): 26-45.
Onieva, E., V. Milanés, C. González, T. de Pedro, J. Pérez, and J. Alonso. 2010. Throttle and Brake Pedals Automation for Populated Areas. Robotica. 28(4): 509-516.
Foulidinejad, Ni., Na. Foulidinejad, J. M. Taib, M. K. Abd Jalil. 2011. Intelligent Traffic Flow Simulation System On A Small Scale Environment. ISI proceeding 4th, International Conference on Computer and Electrical Engineering, Singapore. ASME Press. 27-32.
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