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Paper Abstract and Keywords
Presentation 2024-11-29 10:25
Blind spot estimation in multi-LiDAR network with deep learning model trained by geometry-based automatic annotation
Jumpei Negishi, Ryoichi Shinkuma, Gabriele Trovato (SIT) SRW2024-39 SeMI2024-51
Abstract (in Japanese) (See Japanese page) 
(in English) In modern society, addressing the challenges of last-mile transportation has become essential due to social issues such as labor shortages, an aging population, and the need for decarbonization. As a result, the demand for micro-mobility has been increasing as an effective transportation solution. Additionally, the utilization of digital twins is expected to play a critical role in enabling the autonomous driving of numerous micro-mobility vehicles. By constructing a digital twin using infrastructure-based LiDAR sensor networks, it becomes possible to integrate and analyze data in real time, detect potential hazards, predict risks, and enhance safety. However, to ensure the safe autonomous driving of numerous micro-mobility vehicles, it is necessary to detect blind spots where vehicles cannot see each other. In traditional methods, there is a trade-off between detection accuracy and processing time. To address this issue, we present a real-time blind spot detection technique using deep learning with a 3D sensor network. This report demonstrates that Our system can comprehensively detect blind spots from arbitrary viewpoints and enable real-time predictions.
Keyword (in Japanese) (See Japanese page) 
(in English) Blind spot / Digital twin / 3D sensing / LIDAR sensor network / Point cloud / Spatial feature / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 277, SeMI2024-51, pp. 55-56, Nov. 2024.
Paper # SeMI2024-51 
Date of Issue 2024-11-21 (SRW, 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 SRW2024-39 SeMI2024-51

Conference Information
Committee SRW SeMI  
Conference Date 2024-11-28 - 2024-11-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Univ. of Science Katsushika Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IoT Workshop 
Paper Information
Registration To SeMI 
Conference Code 2024-11-SRW-SeMI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Blind spot estimation in multi-LiDAR network with deep learning model trained by geometry-based automatic annotation 
Sub Title (in English)  
Keyword(1) Blind spot  
Keyword(2) Digital twin  
Keyword(3) 3D sensing  
Keyword(4) LIDAR sensor network  
Keyword(5) Point cloud  
Keyword(6) Spatial feature  
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Keyword(8)  
1st Author's Name Jumpei Negishi  
1st Author's Affiliation Shibaura Institute of Technology (SIT)
2nd Author's Name Ryoichi Shinkuma  
2nd Author's Affiliation Shibaura Institute of Technology (SIT)
3rd Author's Name Gabriele Trovato  
3rd Author's Affiliation Shibaura Institute of Technology (SIT)
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Speaker Author-1 
Date Time 2024-11-29 10:25:00 
Presentation Time 25 minutes 
Registration for SeMI 
Paper # SRW2024-39, SeMI2024-51 
Volume (vol) vol.124 
Number (no) no.276(SRW), no.277(SeMI) 
Page pp.55-56 
#Pages 2 
Date of Issue 2024-11-21 (SRW, SeMI) 

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