| Presentation | 2020-12-11 An Automated Driving Strategy for Microscopic Road Traffic Using Multi-Agent Deep Reinforcement Learning Ryota Suwa, Toshiharu Sugawara, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This study proposes a method for interaction-aware automated driving in microscopic road traffic simulations using multi-agent deep reinforcement learning. In recent years, so many studies proposed an algorithm for automated driving using reinforcement learning, and some companies conducted experimental self-driving on public roads. However, existing reinforcement learning algorithms do not sufficiently consider interactions with other vehicles, where it is essential for safe driving on public roads. Therefore, we introduced a safety-oriented success rate and proposed a method that considers interactions between multiple agents in microscopic traffic environments. We also conducted an experimental evaluation in a driving simulation environment to see if the proposed method can improve self-driving safety. One experimental results suggest that it can achieve a higher success rate in microscopic road traffic situations, such as at an intersection and a junction, than existing methods that used single-agent reinforcement learning. Finally, we discuss the reasons for the improvement and future issues. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Multi-Agent / Deep Reinforcement Learning / Self-Driving |
| Paper # | AI2020-7 |
| Date of Issue | 2020-12-03 (AI) |
| Conference Information | |
| Committee | AI |
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| Conference Date | 2020/12/10(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online and HAMAMATSU ACT CITY |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Foundations and application technologies for AI systems on the new normal |
| Chair | Naoki Fukuta(Shizuoka Univ.) |
| Vice Chair | Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST) |
| Secretary | Yuichi Sei(Nagoya Inst. of Tech.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Automated Driving Strategy for Microscopic Road Traffic Using Multi-Agent Deep Reinforcement Learning |
| Sub Title (in English) | |
| Keyword(1) | Multi-Agent |
| Keyword(2) | Deep Reinforcement Learning |
| Keyword(3) | Self-Driving |
| 1st Author's Name | Ryota Suwa |
| 1st Author's Affiliation | Waseda University(Waseda Univ.) |
| 2nd Author's Name | Toshiharu Sugawara |
| 2nd Author's Affiliation | Waseda University(Waseda Univ.) |
| Date | 2020-12-11 |
| Paper # | AI2020-7 |
| Volume (vol) | vol.120 |
| Number (no) | AI-281 |
| Page | pp.pp.34-38(AI), |
| #Pages | 5 |
| Date of Issue | 2020-12-03 (AI) |