DEVELOPMENT OF A REALISTIC DRIVING BEHAVIOR BY MEANS OF FUZZY INFERENCE SYSTEM

被引:0
|
作者
Fouladinejad, Nima [1 ]
Taib, J. Mohd [1 ]
Jalil, M. K. Abd [1 ]
机构
[1] Univ Teknol Malaysia, Fac Mech Engn, Utm Johor Bahru 81310, Johor, Malaysia
来源
JURNAL TEKNOLOGI | 2015年 / 74卷 / 10期
关键词
Microscopic traffic flow model; intelligent vehicle; Fuzzy Inference System (FIS); realistic decision module;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:69 / 77
页数:9
相关论文
共 50 条
  • [1] Detection of Dangerous Driving Behavior via Fuzzy Inference System
    Liu, Shangzheng
    Zhu, Qinghui
    Wang, Fuzhong
    ICAROB 2018: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS, 2018, : 695 - 698
  • [2] Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition
    Eftekhari, Hamid Reza
    Ghatee, Mehdi
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2018, 58 : 782 - 796
  • [3] Fuzzy C-Means Inference System for Students Classification
    Linawati, Lilik
    Parhusip, Hanna Arini
    SOUTHEAST ASIAN BULLETIN OF MATHEMATICS, 2018, 42 (05) : 647 - 655
  • [4] Development and Evaluation of a Fuzzy Inference System and a Neuro-Fuzzy Inference System for Grading Apple Quality
    Papageorgiou, E. I.
    Aggelopoulou, K.
    Gemtos, T. A.
    Nanos, G. D.
    APPLIED ARTIFICIAL INTELLIGENCE, 2018, 32 (03) : 253 - 280
  • [5] FISDeT: Fuzzy Inference System Development Tool
    Castellano, Giovanna
    Castiello, Ciro
    Pasquadibisceglie, Vincenzo
    Zaza, Gianluca
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2017, 10 (01) : 13 - 22
  • [6] FISDeT: Fuzzy Inference System Development Tool
    Giovanna Castellano
    Ciro Castiello
    Vincenzo Pasquadibisceglie
    Gianluca Zaza
    International Journal of Computational Intelligence Systems, 2017, 10 : 13 - 22
  • [7] TV Series Recommendation Using Fuzzy Inference System, K-Means Clustering and Adaptive Neuro Fuzzy Inference System
    Ahmed, Muyeed
    Paul, Anirudha
    Imtiaz, Mir Tahsin
    Hassan, Md. Zahid
    Ashraf, Shawon
    Rahman, Rashedur M.
    2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2017,
  • [8] Evaluation of Preferred Automated Driving Patterns Based on a Driving Propensity Using Fuzzy Inference System
    Hwang, Sooncheon
    Lee, Dongmin
    JOURNAL OF ADVANCED TRANSPORTATION, 2024, 2024
  • [9] Spectral Fizeau Interferometer spectra processing by means of a fuzzy inference system
    Antonacci, Julian
    Meschino, Gustavo J.
    Passoni, Lucia I.
    Arenas, Gustavo F.
    2015 XVI WORKSHOP ON INFORMATION PROCESSING AND CONTROL (RPIC), 2015,
  • [10] Development of the Fitness Consulting System Based on the Fuzzy Inference
    Huang, Yuchun
    Li, Guoqing
    Wang, Yongqing
    2011 AASRI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRY APPLICATION (AASRI-AIIA 2011), VOL 3, 2011, : 169 - 171