研究者詳細 - 武田　一哉

論文 - 武田　一哉

分割表示 689 件中 81 - 100 件目／全件表示 >>

Automatic Generation of Road Trip Summary Video for Reminiscence and Entertainment using Dashcam Video 査読有り

Bito Kana, Siio Itiro, Ishiguro Yoshio, Takeda Kazuya

AUTOMOTIVEUI '21: 13TH INTERNATIONAL ACM CONFERENCE ON AUTOMOTIVE USER INTERFACES AND INTERACTIVE VEHICULAR APPLICATIONS 頁： 181 - 190 2021年

Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision 査読有り

Karlsson Robin, Wong David Robert, Thompson Simon, Takeda Kazuya

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) 頁： 812 - 819 2021年

A recognition phase Intervention Interface to Improve Naturalness of Autonomous Driving for Distracted Drivers 査読有り

Kuribayashi Atsushi, Takeuchi Eijiro, Carballo Alexander, Ishiguro Yoshio, Takeda Kazuya

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) 頁： 1737 - 1744 2021年

Visibility Estimation in Complex, Real-World Driving Environments Using High Definition Maps 査読有り

Narksri Patiphon, Darweesh Hatem, Takeuchi Eijiro, Ninomiya Yoshiki, Takeda Kazuya

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) 頁： 2847 - 2854 2021年

OpenPlanner 2.0: The Portable Open Source Planner for Autonomous Driving Applications 査読有り

Darweesh Hatem, Takeuchi Eijiro, Takeda Kazuya

2021 IEEE INTELLIGENT VEHICLES SYMPOSIUM WORKSHOPS (IV WORKSHOPS) 頁： 313 - 318 2021年

Characterization of Multiple 3D LiDARs for Localization and Mapping Performance using the NDT Algorithm 査読有り

Carballo Alexander, Monrroy Abraham, Wong David, Narksri Patiphon, Lambert Jacob, Kitsukawa Yuki, Takeuchi Eijiro, Kato Shinpei, Takeda Kazuya

2021 IEEE INTELLIGENT VEHICLES SYMPOSIUM WORKSHOPS (IV WORKSHOPS) 頁： 327 - 334 2021年

Eagleye: A Lane-Level Localization Using Low-Cost GNSS/IMU 査読有り

Takanose Aoki, Kitsukawa Yuki, Megruo Junichi, Takeuchi Eijiro, Carballo Alexander, Takeda Kazuya

2021 IEEE INTELLIGENT VEHICLES SYMPOSIUM WORKSHOPS (IV WORKSHOPS) 頁： 319 - 326 2021年

A Comparison of Methods for Sharing Recognition Information and Interventions to Assist Recognition in Autonomous Driving System 査読有り

Kuribayashi Atsushi, Takeuchi Eijiro, Carballo Alexander, Ishiguro Yoshio, Takeda Kazuya

2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) 頁： 622 - 629 2021年

How to monitor multiple autonomous vehicles remotely with few observers: An active management method 査読有り

Ding Ming, Takeuchi Eijiro, Ishiguro Yoshio, Ninomiya Yoshiki, Kawaguchi Nobuo, Takeda Kazuya

2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) 頁： 1168 - 1173 2021年

Anomalous Sound Detection Using a Binary Classification Model and Class Centroids

Kuroyanagi Ibuki, Hayashi Tomoki, Takeda Kazuya, Toda Tomoki

29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021) 頁： 1995 - 1999 2021年

A Method for Location Initialization of Handheld Devices using Autonomous Driving Vehicles for Interactive Systems

Ishiguro Yoshio, Takeda Kazuya

AUTOMOTIVEUI '21 ADJUNCT PROCEEDINGS: 13TH INTERNATIONAL ACM CONFERENCE ON AUTOMOTIVE USER INTERFACES AND INTERACTIVE VEHICULAR APPLICATIONS 頁： 102 - 106 2021年

Cross-Lingual Voice Conversion using a Cyclic Variational Auto-encoder and a WaveNet Vocoder 査読有り

Hikaru Nakatani, Patrick Lumban Tobing, Kazuya Takeda, Tomoki Toda

2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings 頁： 520 - 526 2020年12月

掲載種別：研究論文（国際会議プロシーディングス）

© 2020 APSIPA. We propose a novel, cross-lingual voice conversion (VC) method using a cyclic variational auto-encoder (CycleVAE). Voice conversion is the transformation of the voice of one speaker into the voice of another speaker, while cross-lingual VC performs voice conversion between speakers who speak different languages. When using VC methods based on parallel learning, it is necessary to prepare accented speech uttered by the source or target speaker, using the pronunciation system of the speaker's mother tongue. On the other hand, VC methods which use a non-parallel learning approach can utilize the natural speech data of both the source and target speakers, produced in their own native languages. It then becomes necessary, however, to deal with the issues of time-alignment and language mismatches. To address these issues, we apply CycleVAE to cross-lingual VC as a sophisticated, non-parallel method of VC. We also apply the WaveNet vocoder in the waveform generation process of CycleVAE-VC to improve overall conversion quality. Our objective and subjective experimental results when performing cross-lingual VC from a native English speaker to a native Japanese speaker confirm that the proposed method achieves a higher level of naturalness and speaker similarity than a conventional RNN-based parallel VC method using accented speech.

Scopus

その他リンク： https://dblp.uni-trier.de/conf/apsipa/2020

Generation of Origami Folding Animations from 3D Point Cloud Using Latent Space Interpolation 査読有り

Chiaki Nakagaito, Takanori Nishino, Kazuya Takeda

SIGGRAPH Asia 2020 Posters. SA 2020 頁： 30 - 2 2020年12月

Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles 査読有り

Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

CoRR abs/2011.13588 巻 2020年11月

掲載種別：研究論文（学術雑誌）

Rich semantic information extraction plays a vital role on next-generation
intelligent vehicles. Currently there is great amount of research focusing on
fundamental applications such as 6D pose detection, road scene semantic
segmentation, etc. And this provides us a great opportunity to think about how
shall these data be organized and exploited.
In this paper we propose road scene graph,a special scene-graph for
intelligent vehicles. Different to classical data representation, this graph
provides not only object proposals but also their pair-wise relationships. By
organizing them in a topological graph, these data are explainable,
fully-connected, and could be easily processed by GCNs (Graph Convolutional
Networks). Here we apply scene graph on roads using our Road Scene Graph
dataset, including the basic graph prediction model. This work also includes
experimental evaluations using the proposed model.

arXiv

その他リンク： http://arxiv.org/pdf/2011.13588v1

CONFORMER-BASED SOUND EVENT DETECTION WITH SEMI-SUPERVISED LEARNING AND DATA AUGMENTATION 査読有り

Koichi Miyazaki, Tatsuya Komatsu, Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda

Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE Workshop) 頁： 100 - 104 2020年11月

Point Grid Map-Based Mid-To-Mid Driving without Object Detection 査読有り

Shunya Seiya, Alexander Carballo, Eijiro Takeuchi, and Kazuya Takeda

2020 IEEE Intelligent Vehicles Symposium（IV） 2020年10月

Trajectory prediction with imitation learning reflecting defensive evaluation in team sports 査読有り

Masakiyo Teranishi, Keisuke Fujii, Kazuya Takeda

2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 頁： 124 - 125 2020年10月

掲載種別：研究論文（国際会議プロシーディングス）

© 2020 IEEE. In team sports, qualitative video analysis is mainly used for decision making of team tactics because of the complex interaction. Among the quantitative analyses, most of studies in team trajectory generation have been evaluated only on prediction errors and did not take tactical evaluations (e.g., good defense) into consideration. In this paper, we propose a trajectory prediction method incorporating the defensive evaluation (i.e., how well they protect the goal) into multi-agent imitation learning model. Although the proposed method had the similar prediction performance to the existing method, our method generated an improved trajectory in terms of defensive evaluation.

DOI： 10.1109/GCCE50665.2020.9291841

Scopus

その他リンク： https://dblp.uni-trier.de/db/conf/gcce/gcce2020.html#TeranishiFT20

Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning 査読有り

Sama Kyle, Morales Yoichi, Liu Hailong, Akai Naoki, Carballo Alexander, Takeuchi Eijiro, Takeda Kazuya

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 69 巻 ( 9 ) 頁： 9315 - 9329 2020年9月

Policy learning with partial observation and mechanical constraints for multi-person modeling 査読有り

Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

CoRR abs/2007.03155 巻 2020年7月

掲載種別：研究論文（学術雑誌）

Extracting the rules of real-world biological multi-agent behaviors is a
current challenge in various scientific and engineering fields. Biological
agents generally have limited observation and mechanical constraints; however,
most of the conventional data-driven models ignore such assumptions, resulting
in lack of biological plausibility and model interpretability for behavioral
analyses in biological and cognitive science. Here we propose sequential
generative models with partial observation and mechanical constraints, which
can visualize whose information the agents utilize and can generate
biologically plausible actions. We formulate this as a decentralized
multi-agent imitation learning problem, leveraging binary partial observation
models with a Gumbel-Softmax reparameterization and policy models based on
hierarchical variational recurrent neural networks with physical and
biomechanical constraints. We investigate the empirical performances using
real-world multi-person motion datasets from basketball and soccer games.

arXiv

その他リンク： http://arxiv.org/pdf/2007.03155v1

Personalized Subjective Driving Risk: Analysis and Prediction 査読有り

Bao Naren, Carballo Alexander, Miyajima Chiyomi, Takeuchi Eijiro, Takeda Kazuya

JOURNAL OF ROBOTICS AND MECHATRONICS 32 巻 ( 3 ) 頁： 503-519 2020年6月

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論文 - 武田　一哉