Updated on 2022/09/16

写真a

 
YAMADA Shunya
 
Organization
Institutes of Innovation for Future Society Mobility Research Course Designated assistant professor
Title
Designated assistant professor

Degree 1

  1. Doctor (Informatics) ( 2020.3   Nagoya University ) 

Research Interests 1

  1. Spatial sensing, Data fusion

Education 2

  1. Nagoya University

    2017.4 - 2020.3

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    Country: Japan

  2. Kyoto Institute of Technology

    2015.4 - 2017.3

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    Country: Japan

Awards 1

  1. Best Paper Award

    2020.4   IARIA   A Vehicle Position Estimation Method Combining Roadside Vehicle Detector and In-Vehicle Sensors

    Shunya Yamada, Yousuke Watanabe, Hiroaki Takada

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    Award type:Award from international society, conference, symposium, etc.  Country:Portugal

 

Papers 10

  1. Tracking Pedestrians Under Occlusion in Parking Space

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

    Computer Systems Science and Engineering   Vol. 44 ( 3 ) page: 2109 - 2127   2023

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    Language:Japanese   Publisher: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   Vol. 20 ( 2 ) page: 422 - 432   2022.8

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    Language:Japanese   Publisher: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   Vol. 74 ( 5 ) page: 1142 - 1160   2021.9

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    Language:Japanese   Publisher: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   Vol. 8 ( 14 ) page: 11548 - 11560   2021.7

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    Language:Japanese   Publisher: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   Vol. 8 ( 4 ) page: 187 - 199   2021

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    Language:Japanese   Publisher: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)     page: 784 - 789   2021

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    Language:Japanese   Publisher: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. Distributed Environmental Information Management System for Autonomous Vehicles using Edge Computing

    Yamada Shunya, Watanabe Yousuke, Takada Hiroaki

    Journal of the Robotics Society of Japan   Vol. 38 ( 2 ) page: 199 - 209   2020

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    Language:Japanese   Publisher:The Robotics Society of Japan  

    <p>In order for autonomous vehicles to drive safely and comfortably, environmental information detected by sensors need to be gathered from wider area and to be more accurate. We can improve data accuracy by fusions of sensor data from not only vehicles but also road infrastructures. Fusion processing is usually performed in a high-performance server (centralized system). However, when the number of sensors is enormous, processing time and communication time for fusions become unacceptable due to high-load and limited capacity of network. And, waiting data-arrivals from all sensors is impractical in such situations. Thus, fusions should be distributed and incrementally updated on each data-arrival. In this paper, we propose a distributed environmental information management system using edge computing and a sensor fusion method.Since the system is composed of geographically distributed edges and a centralized cloud, it can distribute processing costs and communication costs of fusions to the edges and the cloud. The proposal sensor fusion method can incrementally compute intermediate results without waiting for receiving all the environmental information. In comparison experiments with the centralized system, the proposed system improved the efficiency of data processing and reduced the amount of communication data. </p>

    DOI: 10.7210/jrsj.38.199

    CiNii Research

  8. Empty Probability Distribution Considering Measurement Characteristics of Two-dimensional LRF for Autonomous Mobile Robots

    Yamada Shunya, Watanabe Yousuke, Takada Hiroaki

    Journal of the Robotics Society of Japan   Vol. 38 ( 4 ) page: 379 - 390   2020

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    Language:Japanese   Publisher:The Robotics Society of Japan  

    <p>A wide range of accurate environmental information is needed in order for autonomous mobile robots to move safely. An intelligent space, which contains multiple sensors attached to the environment, is useful for information provision. Many optical sensors such as Laser Range Finders (LRFs) are placed to monitor fixed areas. But, LRFs occasionally miss objects due to distance attenuation of lasers, irregular reflection of lasers, and gaps between lasers. Thus, possibility of these detection failures of LRFs should be considered. In this paper, a novel measurement model with properties of the two-dimensional LRFs is constructed, and the empty probability distribution created by the model are proposed. We set two-dimensional LRFs up on a roadside, and calculated an empty probability distribution to evaluate the utility of the proposed model. In addition, since intelligent spaces often contain multiple LRFs, data measured from different viewpoints can be fused to improve data accuracy. We also evaluated sensor-fusion of multiple empty probability distributions obtained from individual LRFs. The fusion result grasps a location at which a detection failure of LRF has occurred. The usefulness of the proposed method was confirmed in our experimental evaluation. </p>

    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   Vol. 13 ( 3&4 ) page: 82 - 93   2020

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

  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)     page: 284 - 287   2019

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    Language:Japanese  

    Web of Science

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