Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Motion Analysis and Performance Improved Method for 3D LiDAR Sensor Data Compression Reviewed

Chenxi Tu, Eijiro Takeuchi, Alexander Carballo, Chiyomi Miyajima, Kazuya Takeda

IEEE Transactions on Intelligent Transportation Systems Vol. 22 ( 1 ) page： 243 - 256 2021.1

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

© 2000-2011 IEEE. Continuous point cloud data is being used more and more widely in practical applications such as mapping, localization and object detection in autonomous driving systems, but due to the huge volume of data involved, sharing and storing this data is currently expensive and difficult. One possible solution is the development of more efficient methods of compressing the data. Other researchers have proposed converting 3D point cloud data into 2D images, or using tree structures to store the data. In a previous study targeting streaming point cloud data, we proposed an MPEG-like compression method which utilizes simultaneous localization and mapping (SLAM) results to simulate LiDAR's operating process. In this paper, instead of imitating MPEG, we propose new strategy for more efficient reference frame distribution and more natural frame prediction, and use a different algorithm to encode the residual, greatly improving the algorithm's performance and its stability in different scenarios. We also discuss how various parameters affect compression performance. Using our proposed method, streaming point cloud data collected by LiDAR sensors can be compressed to 1/50th of its original size, with only 2 cm of Root Mean Square Error for each detected point. We evaluate our proposed method by comparing its performance with several other existing point cloud compression methods in three different driving scenarios, demonstrating that our proposed method outperforms them.

DOI： 10.1109/TITS.2019.2956066

Web of Science

Scopus

How to monitor multiple autonomous vehicles remotely with few observers: An active management method Reviewed

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

2021 IEEE Intelligent Vehicles Symposium (IV) page： 1168 - 1173 2021

RSG-Net: Towards Rich Semantic Relationship Prediction for Intelligent Vehicle in Complex Environment Reviewed

Tian, Yafu; Carballo, Alexander; Li, Ruifeng; Takeda, Kazuya;

2021 IEEE Intelligent Vehicles Symposium (IV) page： 546 - 552 2021

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

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

2021 IEEE Intelligent Vehicles Symposium (IV) page： 622 - 629 2021

Automatic Generation of Road Trip Summary Video for Reminiscence and Entertainment using Dashcam Video Reviewed

Kana Bito, Itiro Siio, Yoshio Ishiguro, Kazuya Takeda

13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications page： 181 - 190 2021

Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision Reviewed

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

The 24th IEEE International Conference on Intelligent Transportation Systems(ITSC2021) page： 812 - 819 2021

A recognition phase Intervention Interface to Improve Naturalness of Autonomous Driving for Distracted Drivers Reviewed

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

2021 IEEE International Intelligent Transportation Systems Conference (ITSC) page： 1737 - 1744 2021

Visibility Estimation in Complex, Real-World Driving Environments Using High Definition Maps Reviewed

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

The 24th IEEE International Conference on Intelligent Transportation Systems(ITSC2021) page： 2847 - 2854 2021

OpenPlanner 2.0: The Portable Open Source Planner for Autonomous Driving Applications Reviewed

Hatem Darweesh , Eijiro Takeuchi , and Kazuya Takeda

IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) page： 313 - 318 2021

Characterization of Multiple 3D LiDARs for Localization and Mapping Performance using the NDT Algorithm Reviewed

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

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

Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE

In this work, we present a detailed comparison of ten different 3D LiDAR sensors for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware. LiDAR data used in this study is a subset of our LiDAR Benchmarking and Reference (LIBRE) dataset, captured independently from each sensor, from a vehicle driven on public urban roads multiple times, at different times of the day. In this study, we analyze the performance and characteristics of each LiDAR for the tasks of (1) 3D mapping including an assessment map quality based on mean map entropy, and (2) 6-DOF localization using a ground truth reference map.

DOI： 10.1109/IVWorkshops54471.2021.9669244

Web of Science

Eagleye: A Lane-Level Localization Using Low-Cost GNSS/IMU Reviewed

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

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) page： 319 - 326 2021

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) page： 1995 - 1999 2021

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 page： 102 - 106 2021

Cross-Lingual Voice Conversion using a Cyclic Variational Auto-encoder and a WaveNet Vocoder Reviewed

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 page： 520 - 526 2020.12

Publishing type：Research paper (international conference proceedings)

© 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

Other Link： https://dblp.uni-trier.de/conf/apsipa/2020

Generation of Origami Folding Animations from 3D Point Cloud Using Latent Space Interpolation Reviewed

Chiaki Nakagaito, Takanori Nishino, Kazuya Takeda

SIGGRAPH Asia 2020 Posters. SA 2020 page： 30 - 2 2020.12

Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles Reviewed

Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

CoRR Vol. abs/2011.13588 2020.11

Publishing type：Research paper (scientific journal)

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

Other Link： http://arxiv.org/pdf/2011.13588v1

CONFORMER-BASED SOUND EVENT DETECTION WITH SEMI-SUPERVISED LEARNING AND DATA AUGMENTATION Reviewed

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

Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE Workshop) page： 100 - 104 2020.11

Point Grid Map-Based Mid-To-Mid Driving without Object Detection Reviewed

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

2020 IEEE Intelligent Vehicles Symposium（IV） 2020.10

Trajectory prediction with imitation learning reflecting defensive evaluation in team sports Reviewed

Masakiyo Teranishi, Keisuke Fujii, Kazuya Takeda

2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 page： 124 - 125 2020.10

Publishing type：Research paper (international conference proceedings)

© 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

Other Link： https://dblp.uni-trier.de/db/conf/gcce/gcce2020.html#TeranishiFT20

Extracting Human-Like Driving Behaviors from Expert Driver Data Using Deep Learning Reviewed

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

IEEE Transactions on Vehicular Technology Vol. 69 ( 9 ) page： 9315 - 9329 2020.9

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

© 1967-2012 IEEE. This paper introduces a method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors. Deep learning techniques were used to extract latent features from the collected data. Extracted features were clustered into behaviors and used to create velocity profiles allowing an autonomous driving agent could drive in a human-like manner. By using proactive driving behaviors, the agent could limit potential sources of discomfort such as jerk and uncomfortable velocities. Additionally, we proposed a method to compare trajectories where not only the geometric similarity is considered, but also velocity, acceleration and jerk. Experimental results in a simulator implemented in ROS show that the autonomous agent built with the driving behaviors was capable of driving similarly to expert human drivers.

DOI： 10.1109/TVT.2020.2980197

Web of Science

Scopus

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Papers - TAKEDA, Kazuya