mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

Suzumura, Toyotaro; Sugiki, Akiyoshi; Takizawa, Hiroyuki; Imakura, Akira; Nakamura, Hiroshi; Taura, Kenjiro; Kudoh, Tomohiro; Hanawa, Toshihiro; Sekiya, Yuji; Kobayashi, Hiroki; Matsushima, Shin; Kuga, Yohei; Nakamura, Ryô; Jiang, Renhe; Kawase, Junya; Hanai, Masahiro; Miyazaki, Hiroshi; Ishizaki, Takayoshi; Shimotoku, Daisuké; Miyamoto, Daisuke; Aida, Kô; Takefusa, Atsuko; Kurimoto, Takashi; Sasayama, Koji; Kitagawa, Naoya; Fujiwara, Ikki; Tanimura, Yusuke; Aoki, Takayuki; Endo, Tetsuro; Ohshima, Satoshi; Fukazawa, Keiichiro; Date, Susumu; Uchibayashi, Toshihiro

doi:10.48550/arxiv.2203.14188

Cited by 3 publications

(2 citation statements)

References 13 publications

(16 reference statements)

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“…In this study, the data utilization society creation platform MDX [55] was used as the computer resource. In detail, an instance with 100 virtual CPUs and 160 GB of memory was built on MDX and processed in parallel for approximately 10 days to generate pseudo-people-flow data for the entire country.…”

Section: Data Processingmentioning

confidence: 99%

Pseudo-Pflow: Development of Nationwide Synthetic Open Dataset for People Movement Based on Limited Travel Survey and Open Statistical Data

Kashiyama¹,

Pang²,

Sekimoto³

et al. 2022

SSRN Journal

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Section: Data Processingmentioning

confidence: 99%

Pseudo-Pflow: Development of Nationwide Synthetic Open Dataset for People Movement Based on Limited Travel Survey and Open Statistical Data

Kashiyama¹,

Pang²,

Sekimoto³

et al. 2022

SSRN Journal

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“…We use the base model of YOLOv7 with pre-trained weights on the COCO dataset with an input size of 640×640. We first train with the real-world images from the vehicle orientation dataset [4] for 100 epochs with a learning rate of 0.001 on four Tesla A100 GPUs [16] and then use the synthetic vehicle orientation dataset [6] to fine-tune the next ten epochs with a reduced learning rate of 0.0001 to prevent large changes in the parameters. It should be noted that the vehicle orientation dataset contains 15 classes of vehicles, while the synthetic vehicle orientation dataset has 12 classes of vehicles due to the absence of bus class; thus, bus front, bus back, and bus side classes are not present.…”

Section: B Carla Reid Datasetmentioning

confidence: 99%

Vehicle re-identification and trajectory reconstruction using multiple moving cameras in the CARLA driving simulator

Kumar

Kashiyama

Maeda³

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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Analyzing vehicle movement trajectories is essential for understanding urban mobility and traffic flow patterns. Obtaining a reliable estimate of vehicle trajectory is challenging as it requires the vehicle to be observed and re-identified at different locations and times. Recently, a scalable citywide traffic flow estimation method has been proposed utilizing moving cameras on vehicle dashboards. As moving cameras constantly interact with their surroundings, they provide valuable information about the urban environment that can be used to estimate vehicle trajectories. This study extends the recently proposed method for traffic flow estimation by using cameras mounted on multiple moving observers to reconstruct the trajectory of detected vehicles. We develop the CARLA ReID dataset, which includes more than 50,000 images taken from 85 cameras for over 700 different vehicle models, and train a re-identification network to identify the same vehicle by multiple observers. Utilizing our proposed methodology, we conduct extensive research to estimate trajectories of vehicles in a driving simulator CARLA and evaluate the accuracy of reconstructed trajectories using Symmetrized Segment-Path Distance (SSPD) and Hausdorff Distance metrics. Our proposed method achieves a mean error of 5.13 meters evaluated using the SSPD metric for ten driving experiments in CARLA. Findings from this study will provide valuable insights for conducting traffic flow research in a simulation environment, which is otherwise challenging and costly in practice.

show abstract

Vehicle re-identification and trajectory reconstruction using multiple moving cameras in the CARLA driving simulator

Kumar¹,

Kashiyama²,

Maeda³

et al. 2022

Preprint

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<p>Analyzing vehicle movement trajectories is essential for understanding urban mobility and traffic flow patterns. Obtaining a reliable estimate of vehicle trajectory is challenging as it requires the vehicle to be observed and re-identified at different locations and times. Recently, a scalable citywide traffic flow estimation method has been proposed utilizing moving cameras on vehicle dashboards. As moving cameras constantly interact with their surroundings, they provide valuable information about the urban environment that can be used to estimate vehicle trajectories. This study extends the recently proposed method for traffic flow estimation by using cameras mounted on multiple moving observers to reconstruct the trajectory of detected vehicles. We develop the CARLA ReID dataset, which includes more than 50,000 images taken from 85 cameras for over 700 different vehicle models, and train a re-identification network to identify the same vehicle by multiple observers. Utilizing our proposed methodology, we conduct extensive research to estimate trajectories of vehicles in a driving simulator CARLA and evaluate the accuracy of reconstructed trajectories using Symmetrized Segment-Path Distance (SSPD) and <em>Hausdorff</em> Distance metrics. Our proposed method achieves a mean error of 5.13 meters evaluated using the SSPD metric for ten driving experiments in CARLA. Findings from this study will provide valuable insights for conducting traffic flow research in a simulation environment, which is otherwise challenging and costly in practice.</p>

show abstract

mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

Cited by 3 publications

References 13 publications

Pseudo-Pflow: Development of Nationwide Synthetic Open Dataset for People Movement Based on Limited Travel Survey and Open Statistical Data

Pseudo-Pflow: Development of Nationwide Synthetic Open Dataset for People Movement Based on Limited Travel Survey and Open Statistical Data

Vehicle re-identification and trajectory reconstruction using multiple moving cameras in the CARLA driving simulator

Vehicle re-identification and trajectory reconstruction using multiple moving cameras in the CARLA driving simulator

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