書誌事項
- タイトル別名
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- Traffic Simulation with Multi-agent and Deep Reinforcement Learning
抄録
<p>Series of traffic accidents and traffic congestions happen every day in big cities like Tokyo. Therefore, it’s necessary to simulate the traffic condition and research on a method to control vehicles’ behavior well. Besides, autonomous driving is being developed rapidly nowadays and researchers often use deep learning to study trajectory prediction and path planning for autonomous vehicles. In this research, we use the shortest path search algorithm and deep reinforcement learning to control vehicles’ behavior in a traffic simulator SUMO. Regarding the local behavior which contains their speed and acceleration, we utilized deep reinforcement learning to control it. Regarding global behavior, which is path planning, we used a method combining Dijkstra algorithm and deep reinforcement learning. The vehicle agents in the simulator have better behavior after training. They can have acceleration and path selection that shorten their driving time when they encounter different traffic situations.</p>
収録刊行物
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- 計算力学講演会講演論文集
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計算力学講演会講演論文集 2023.36 (0), OS-1904-, 2023
一般社団法人 日本機械学会
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詳細情報 詳細情報について
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- CRID
- 1390018451149652608
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- ISSN
- 24242799
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可