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Title
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Cognitive Route Planning in Vehicle using Artificial Neural Network
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Author
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Yousaf Saeed, Khalil Ahmed, Sagheer Abbas, Atifa Athar, Asim Zeb
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| Citation |
Vol. 26 No. 3 pp. 59-64
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Abstract
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Department of transportation worldwide is facing various challenges despite introducing and incorporating number of vehicular features. However, addressing human cognition in vehicle is very much necessary for the future so that vehicle can provide human-like decisions in different situations. In this regard, providing accurate, shortest, timely and optimal route information to the driver of vehicle in a cognitive manner is indeed important. This is different from the current vehicle route information schemes that is provided on demand, is typically static and do not evolve. The proposed cognitive scheme discussed in this paper enables a vehicle to provide optimal route information to the driver by utilizing cognitive parameters. We have proposed route planning scheme to be planned by vehicle itself from cognitive perspective where cognitive memories and cognitive variables both are utilized. Cognitive memory is going to be essential for storing route experiences related to different task environments. Also, route episodes are learned, stored and accessed later inside cognitive memory for optimal use. This enables a vehicle to evolve itself with route information and provide rational routes. Cognitive variables facilitates the route related planning approach in this regard. The more a vehicle plan routes, the more it populates its knowledge repository with time. The goal is to make a shift from supervised learning mode towards unsupervised ultimately. Moreover, in the proposed scheme, concepts of controllers are introduced from system administration point of view for performance enhancement, and for validation of the proposed scheme, artificial neural network is used to highlight that learning can be achieved in vehicular agency for providing optimal routes cognitively.
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Keywords
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Vehicle route, GPS, VANET, ANN, Cognitive Memories
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URL
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http://paper.ijcsns.org/07_book/202603/20260307.pdf
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