DocumentCode :
2580067
Title :
Developing adaptive driving route guidance systems based on fuzzy neural network
Author :
Lin, I-Cheng ; Chou, Shuo-Yan ; Hsu, Hsin-Yin
Author_Institution :
TFT-LCD Manuf. Div., Production Planning Dept., TFT BU, Taipei, Taiwan
fYear :
2009
fDate :
11-14 Oct. 2009
Firstpage :
4293
Lastpage :
4298
Abstract :
This paper proposes a route guidance system which can be moldable by drivers based on using fuzzy neural network. The self-learning system has become a hot issue in the field of Driving Route Guidance. However, most of the researches didn´t pay attention to the relationship between the route choice and the driver´s vague perception. Even though with the same starting and destination points, different drivers typically do not go through the same path as they make choices of routes based on different attributes and weighting of the attributes. The adaptive-network-based fuzzy inference system (ANFIS) is used to learn the decision logic with vague attributes from the past driving records of the drivers to make the guidance system adaptive. By integrating this intelligent adaptive capability, a driving route guidance system should be able to adapt to driver´s behavior in generating routes teller-made for individual drivers.
Keywords :
adaptive systems; fuzzy neural nets; road traffic; unsupervised learning; adaptive network based fuzzy inference system; decision logic; drivers vague perception; fuzzy neural network; past driving records; route guidance systems; routes teller made; self learning system; Adaptive control; Adaptive systems; Cybernetics; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Production planning; Shortest path problem; Thin film transistors; USA Councils; ANFIS; TSK inference system; fuzzy inference system; intelligent adaptive system; route choice criteria; route guidance system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1062-922X
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
Type :
conf
DOI :
10.1109/ICSMC.2009.5346804
Filename :
5346804
Link To Document :
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