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http://dx.doi.org/10.5762/KAIS.2018.19.6.639

Decision Support System of Obstacle Avoidance for Mobile Vehicles  

Kang, Byung-Jun (School of Electrical, Electronics & Communication Engineering, KOREATECH)
Kim, Jongwon (Department of Electromechanical Convergence Engineering, KOREATECH)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.19, no.6, 2018 , pp. 639-645 More about this Journal
Abstract
This paper is intended to develop a decision model that can be applied to autonomous vehicles and autonomous mobile vehicles. The developed module has an independent configuration for application in various driving environments and is based on a platform for organically operating them. Each module is studied for decision making on lane changes and for securing safety through reinforcement learning using a deep learning technique. The autonomous mobile moving body operating to change the driving state has a characteristic where the next operation of the mobile body can be determined only if the definition of the speed determination model (according to its functions) and the lane change decision are correctly preceded. Also, if all the moving bodies traveling on a general road are equipped with an autonomous driving function, it is difficult to consider the factors that may occur between each mobile unit from unexpected environmental changes. Considering these factors, we applied the decision model to the platform and studied the lane change decision system for implementation of the platform. We studied the decision model using a modular learning method to reduce system complexity, to reduce the learning time, and to consider model replacement.
Keywords
Autonomous Vehicles; Deep Learning; DQN; Modular Network; Reinforcement Learning;
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