記述言語：日本語   出版者・発行元：IEEE Internet of Things Journal  

The Internet of Things plays an indispensable role in the development of connected vehicles, which will pave the way for road safety applications. In recent years, the concept of a cooperative collision warning system (CCWS) has been introduced and developed to enhance road safety, and it has been seen as a typical Internet-of-Vehicles application. In most CCWSs, it is vital to have a detection mechanism based on trajectory predictions where the uncertainties associated with vehicular state and motion are complex. However, most available approaches in this regard did not consider these uncertainties. Hence, this article proposes a new collision risk assessment (CRA) method where sigma trajectories that include multiple possible trajectories considering multiple aspects of vehicular motion are designed to cope with vehicular uncertainties. Our method is implemented in a novel server-based architecture, which is different from the commonly used vehicle-based controlled CCWSs. The CRA is provided as a service by a cloud server. The proposed method and architecture are validated and evaluated through extensive real-world experiments. Experimental results show that our method outperforms a referenced method in terms of CRA and achieves better robustness in tolerating communication delays and dropouts. Latencies in CRA service were analyzed, and it was found that powerful computing resources provided by cloud servers can significantly decrease computational cost, which will indirectly compensate for communication costs in the future. Based on our high-performance CRA method, the proposed architecture can be regarded as a novel option for CCWS design.

DOI： 10.1109/JIOT.2021.3059222

Web of Science

Scopus

記述言語：日本語   出版者・発行元：International Journal of Mechatronics and Automation  

Pedestrian detection sensors in road infrastructure and smartphone's built-in sensors have been used to detect and track pedestrians for road safety. Nevertheless, although pedestrian detection sensors in road infrastructure can detect pedestrians' high-precision position, they cannot acquire the accurate attribute information of pedestrians. On the other hand, smartphone sensors can send location information, user identifier, and the attribute information of a user, but it has a significant margin of error in GPS data. The defects of LiDAR and smartphone render acquiring a pedestrian's high-precision location and attribute information simultaneously impossible. Currently, few studies on the simultaneous acquisition of pedestrian high-precision position and attribute information have been conducted. In this paper, the authors propose a pedestrian position and attribute information detecting system to extract both pedestrian high-precision position and attribute information in real-time based on LiDAR and smartphone sensor fusion. Moreover, an experiment is carried out to evaluate the system.

DOI： 10.1504/IJMA.2021.120380

Scopus

記述言語：日本語   出版者・発行元：2021 IEEE International Conference on Mechatronics and Automation, ICMA 2021  

Pedestrian detection sensors in road infrastructure and GPS sensors mounted in smartphones have been used in driving safety support systems for detecting pedestrians. However, although pedestrian detection sensors in road infrastructure can detect pedestrians' high-precision position, they cannot acquire the attribute information of pedestrians. Conversely, a smartphone's built-in sensor can send GPS location information, user identifier, and the attribute information of a user, but it has a margin of error from meters to more than tens of meters in GPS location information. This renders acquiring a pedestrian's high-precision location and attribute information simultaneously impossible. To date, no research on the simultaneous acquisition of pedestrian high-precision position and attribute information has been conducted. To fill this gap, this paper proposes an approach to extract both pedestrian high-precision position and attribute information in real-time based on multi-sensor fusion. Moreover, an experiment is conducted to evaluate the proposed approach.

DOI： 10.1109/ICMA52036.2021.9512649

Web of Science

Scopus

記述言語：日本語   出版者・発行元：一般社団法人 日本ロボット学会  

DOI： 10.7210/jrsj.38.199

CiNii Research

記述言語：日本語   出版者・発行元：一般社団法人 日本ロボット学会  

DOI： 10.7210/jrsj.38.379

CiNii Research

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語  

Web of Science

開催年月日： 2023年9月

開催年月日： 2023年9月 - 2923年9月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：仙台国際センター  

開催年月日： 2022年9月

開催年月日： 2021年12月

開催年月日： 2021年9月

開催年月日： 2021年1月

開催年月日： 2020年9月

開催年月日： 2019年9月

開催年月日： 2018年9月