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| Presentation | 2020-11-26 17:20 [Poster Presentation] A Study of Reinforcement Learning-Based Automatic Driving Control Using Information of Neighboring Vehicles through Inter-vehicle Communication Harada Tomohiro (TMU), Kiyohiko Hattori (TUT)  SeMI2020-34 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, with improvements in AI technology and sensor performance, research on automated driving has become very active. Most of the driving assistance systems on the market use an absolute position estimation system (e.g., GPS) and their own sensors to estimate the location and environment. However, it is not easy to control a vehicle by using only its own sensor information, and it is desirable to actively acquire information about the environment and vehicles in the vicinity by using inter-vehicle communication. In this study, we assume automated vehicles with multiple distance sensors and communication devices, and consider automatic control using information obtained by inter-vehicle communication within the sensing range of the vehicle's own distance sensors. Specifically, we used reinforcement learning, which is a form of machine learning, to investigate how sensor measurement error affects learning and evaluation when automatic control is used in a situation where multiple vehicles are driving simultaneously in an unknown environment. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Vehicle-to-vehicle communication / automatic driving / reinforcement learning / distance sensors / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 261, SeMI2020-34, pp. 75-75, Nov. 2020. | |
| Paper # | SeMI2020-34 | |
| Date of Issue | 2020-11-19 (SeMI) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SeMI2020-34 |
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| Conference Information | |
| Committee | SRW SeMI CNR |
| Conference Date | 2020-11-26 - 2020-11-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2020-11-SRW-SeMI-CNR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of Reinforcement Learning-Based Automatic Driving Control Using Information of Neighboring Vehicles through Inter-vehicle Communication |
| Sub Title (in English) | |
| Keyword(1) | Vehicle-to-vehicle communication |
| Keyword(2) | automatic driving |
| Keyword(3) | reinforcement learning |
| Keyword(4) | distance sensors |
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| Keyword(8) | |
| 1st Author's Name | Harada Tomohiro |
| 1st Author's Affiliation | Tokyo Metropolitan University (TMU) |
| 2nd Author's Name | Kiyohiko Hattori |
| 2nd Author's Affiliation | Tokyo University of Technology (TUT) |
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| Speaker | Author-1 |
| Date Time | 2020-11-26 17:20:00 |
| Presentation Time | 70 minutes |
| Registration for | SeMI |
| Paper # | SeMI2020-34 |
| Volume (vol) | vol.120 |
| Number (no) | no.261 |
| Page | p.75 |
| #Pages | 1 |
| Date of Issue | 2020-11-19 (SeMI) |
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