TY - GEN
T1 - Edge computing system with multi-LIDAR sensor network for robustness of autonomous personal-mobility
AU - Akiyama, Kuon
AU - Shinkuma, Ryoichi
AU - Yamamoto, Chotaro
AU - Saito, Mai
AU - Ito, Toshio
AU - Nihei, Koichi
AU - Iwai, Takanori
N1 - Funding Information:
This work was supported in part by JST PRESTO no. JPMJPR1854, JST SBIR-1 no. JPMJST2151, and JSPS KAK-ENHI no. 21H03427, Japan. The evaluation results were partly obtained from research commissioned by NICT, Japan.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - One of the most important systems for smart cities is an edge computing system to facilitate decision-making by using real-time awareness and data analytics. Although existing works focused on the safety of pedestrians, the safety of personal-mobility vehicles is what smart cities need to address toward the future. On-board sensors could be easily disabled because of external issues of hardware caused by dirt or snow, internal issues of hardware, and software issues such as malware infection. We propose an edge computing system that supports robust vehicle automation with a sensor network using multiple light-detection-and-ranging (LIDAR) sensors deployed like roadside cameras or indoor security cameras for surveillance for personal-mobility vehicles. We develop a prototype system of the proposed system using real LIDAR units and a mobility scooter and evaluate the system in terms of route errors to verify the feasibility of the proposed system with a scenario where the vehicle is enabled to continue self-driving even when its on-board sensor is disabled.
AB - One of the most important systems for smart cities is an edge computing system to facilitate decision-making by using real-time awareness and data analytics. Although existing works focused on the safety of pedestrians, the safety of personal-mobility vehicles is what smart cities need to address toward the future. On-board sensors could be easily disabled because of external issues of hardware caused by dirt or snow, internal issues of hardware, and software issues such as malware infection. We propose an edge computing system that supports robust vehicle automation with a sensor network using multiple light-detection-and-ranging (LIDAR) sensors deployed like roadside cameras or indoor security cameras for surveillance for personal-mobility vehicles. We develop a prototype system of the proposed system using real LIDAR units and a mobility scooter and evaluate the system in terms of route errors to verify the feasibility of the proposed system with a scenario where the vehicle is enabled to continue self-driving even when its on-board sensor is disabled.
KW - 3D sensor network
KW - LIDAR
KW - autonomous vehicle
KW - edge computing system
KW - personal mobility
KW - smart monitoring
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U2 - 10.1109/ICDCSW56584.2022.00061
DO - 10.1109/ICDCSW56584.2022.00061
M3 - Conference contribution
AN - SCOPUS:85143748590
T3 - Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops, ICDCSW 2022
SP - 290
EP - 295
BT - Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops, ICDCSW 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2022
Y2 - 10 July 2022 through 13 July 2022
ER -