2022/09/16 更新

写真a

ヤマダ シュンヤ
山田 峻也
YAMADA Shunya
所属
未来社会創造機構 モビリティ社会研究所 社会的価値研究部門 特任助教
職名
特任助教

学位 1

  1. 博士(情報学) ( 2020年3月   名古屋大学 ) 

研究キーワード 1

  1. 空間センシング,データフュージョン

学歴 2

  1. 名古屋大学   情報学研究科 情報システム学専攻

    2017年4月 - 2020年3月

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    国名: 日本国

  2. 京都工芸繊維大学   工芸科学研究科   機械設計学専攻

    2015年4月 - 2017年3月

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    国名: 日本国

受賞 1

  1. Best Paper Award

    2020年4月   IARIA   A Vehicle Position Estimation Method Combining Roadside Vehicle Detector and In-Vehicle Sensors

    Shunya Yamada, Yousuke Watanabe, Hiroaki Takada

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    受賞区分:国際学会・会議・シンポジウム等の賞  受賞国:ポルトガル共和国

 

論文 10

  1. Tracking Pedestrians Under Occlusion in Parking Space

    Zhou Z., Yamada S., Watanabe Y., Takada H.

    Computer Systems Science and Engineering   44 巻 ( 3 ) 頁: 2109 - 2127   2023年

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    記述言語:日本語   出版者・発行元:Computer Systems Science and Engineering  

    Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians' sudden appearance in the vehicle's path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision digital map of the parking lot, pedestrians' smart device's sensing data, and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot. However, this subject has not been studied and explored in existing studies. To fill this void, this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces. We also evaluate the proposed method through real-world experiments. The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy. It can also be used for pedestrian tracking in parking spaces.

    DOI: 10.32604/csse.2023.029005

    Scopus

  2. Estimation Method of Parking Space Conditions Using Multiple 3D-LiDARs

    Yamada Shunya, Watanabe Yousuke, Kanamori Ryo, Sato Kenya, Takada Hiroaki

    INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH   20 巻 ( 2 ) 頁: 422 - 432   2022年8月

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    記述言語:日本語   出版者・発行元:International Journal of Intelligent Transportation Systems Research  

    In the early stages of the spread of autonomous vehicles, it is conceivable to operate an automated valet parking system in parking lots where autonomous vehicles and non-autonomous vehicles coexist. Since non-autonomous vehicles may park beyond the parking space, it is necessary to estimate parking space conditions three-dimensionally. This paper proposes a method to estimate the parking space conditions using multiple 3D-LiDARs that can detect the space three-dimensionally. In the evaluation experiment, multiple 3D-LiDARs were installed in the parking lot of a public facility, and the estimation accuracy of the proposed method was evaluated in various situations.

    DOI: 10.1007/s13177-022-00300-w

    Web of Science

    Scopus

  3. Comparative evaluation of Kalman filters and motion models in vehicular state estimation and path prediction

    Tao Lu, Watanabe Yousuke, Yamada Shunya, Takada Hiroaki

    JOURNAL OF NAVIGATION   74 巻 ( 5 ) 頁: 1142 - 1160   2021年9月

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    記述言語:日本語   出版者・発行元:Journal of Navigation  

    Vehicle state estimation and path prediction, which usually involve Kalman filter and motion model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative performance assessment, regarding accuracy and efficiency, of the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) is rarely discussed. This paper is devoted to empirically evaluating the performance of UKF and EKF incorporating different motion models and investigating the models' properties and the affecting factors in path prediction. Extensive real world experiments have been carried out and the results show that EKF and UKF have roughly identical accuracy in state estimation; however, EKF is faster than UKF generally; the fastest filter is about 2â 6 times faster than the slowest. The path prediction experiments reveal that the velocity estimate and the used motion model affect path prediction; the more realistically the model reflects the vehicle's driving status, the more reliable its predictions.

    DOI: 10.1017/S0373463321000370

    Web of Science

    Scopus

  4. Collision Risk Assessment Service for Connected Vehicles: Leveraging Vehicular State and Motion Uncertainties

    Tao Lu, Watanabe Yousuke, Li Yixiao, Yamada Shunya, Takada Hiroaki

    IEEE INTERNET OF THINGS JOURNAL   8 巻 ( 14 ) 頁: 11548 - 11560   2021年7月

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    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

  5. PAIDS: Toward pedestrian high-precision position and attribute information detection

    Zhou Z., Kitamura S., Watanabe Y., Yamada S., Takada H.

    International Journal of Mechatronics and Automation   8 巻 ( 4 ) 頁: 187 - 199   2021年

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    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

  6. Extraction of Pedestrian Position and Attribute Information Based on the Integration of LiDAR and Smartphone Sensors

    Zhou Zhengshu, Kitamura Saya, Watanabe Yousuke, Yamada Shunya, Takada Hiroaki

    2021 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2021)     頁: 784 - 789   2021年

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    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

  7. エッジコンピューティングを利用した自動運転車のための環境情報分散管理システム

    山田 峻也, 渡辺 陽介, 高田 広章

    日本ロボット学会誌   38 巻 ( 2 ) 頁: 199 - 209   2020年

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    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

  8. 自律移動ロボットのための二次元LRFの計測特性を考慮したEmpty確率分布

    山田 峻也, 渡辺 陽介, 高田 広章

    日本ロボット学会誌   38 巻 ( 4 ) 頁: 379 - 390   2020年

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    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

  9. Evaluation of Vehicle Position Estimation Method Combining Roadside Vehicle Detector and In-vehicle Sensors

    Shunya YAMADA, Yousuke WATANABE, Hiroaki TAKADA

    The International Journal on Advances in Networks and Services   13 巻 ( 3&4 ) 頁: 82 - 93   2020年

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    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)  

  10. Performance Evaluation of Querying Point Clouds in RDBMS

    Ikawa Gen, Watanabe Yousuke, Yamada Shunya, Takada Hiroaki

    2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP)     頁: 284 - 287   2019年

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    記述言語:日本語  

    Web of Science

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