Summary
International Conference on Emerging Technologies for Communications
2020
Session Number:E4
Session:
Number:E4-1
Route suggestion for vehicles inspired by the cognitive process of a human brain and its relation to predictive control of communication networks
Yuichi Ohsita, Masayuki Murata,
pp.-
Publication Date:2020/12/2
Online ISSN:2188-5079
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Summary:
We have proposed a method to identify the current condition from noisy information based on the model of the cognitive process of a human brain, called the Bayesian Attractor Model. In this model, the cognitive options are embedded as attractors. Then, the brain assigns stochastic variables related to the options and recognizes which option is suitable by updating the variables by using the Bayesian inference. We have applied a method based on the Bayesian Attractor Model to the resource allocation within the communication network. In this paper, we apply the method to the control of transportation. Especially, we focus on the system to avoid congestion by suggesting routes. By applying our method based on the cognitive process of a human brain, the system can make decisions even if the information monitored at each time slot is uncertain. Then, based on the identified condition, the system suggests routes to avoid congestion. We also discuss the relation between the system of intelligent transportation and the resource allocation of the communication network. We have proposed a method to allocate resources by predicting future demand on the communication network using the real-world information including the information on the vehicles on the roads. However, intelligent transportation may affect the future positions of vehicles, which have also an impact on the future demand on the communication network. Therefore, we discuss the cooperation between the controller of intelligent transportation and the controller of the communication network.