Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Predictive World Models from Real-World Partial Observations.

Robin Karlsson, Alexander Carballo, Keisuke Fujii 0001, Kento Ohtani, Kazuya Takeda

MOST page： 152 - 166 2023

ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment Reviewed

Robin Karlsson, Tomoki Hayashi, Keisuke Fujii, Alexander Carballo, Kento Ohtani, Kazuya Takeda

The 33rd British Machine Vision Conference (BMVC 2022) 2022.11

Methods of Gently Notifying Pedestrians of Approaching Objects when Listening to Music Reviewed

Yuki Sakashita, Yoshio Ishiguro, Kento Ohtani, Kazuya TakedaTakanori Nishino,

UIST '22: The 35th Annual ACM Symposium on User Interface Software and Technology ( 39 ) page： 1 - 4 2022.10

Improvement of anomalous sound detection method considering the distribution of embedding Reviewed

Ibuki Kuroyanagi, Tomoki Hayashi, Kazuya Takeda, Tomoki Toda

the 24th International Congress on Acoustics page： 5 2022.10

ドライブレコーダを活用した旅の要約動画自動生成システム Reviewed

尾頭花奈, 石黒祥生, 椎尾一郎, 武田一哉

コンピュータソフトウェア Vol. 39 ( 4 ) page： 4_144 - 4_157 2022.10

Automatic Fault Detection in Race Walking From a Smartphone Camera via Fine-Tuning Pose Estimation Reviewed

Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii

2022 IEEE 11th Global Conference on Consumer Electronics 2022.10

Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction Reviewed

Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii,

9th Workshop on Machine Learning and Data Mining for Sports Analytics 2022 (MLSA'22) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD'22) 2022.9

OPTIMIZATION OF AIRCRAFT FLIGHT PATHS CONSIDERING THE CONFLICTING PARAMETERS OF ECONOMY AND SAFETY Reviewed

Tetsuya Nagashima, Ming Ding, Keisuke FUJII & Kazuya TAKEDA

33rd Congress of the International Council of the Aeronautical Sciences 2022.9

Improvement of Serial Approach to Anomalous Sound Detection by Incorporating Two Binary Cross-Entropies for Outlier Exposure Reviewed

Ibuki Kuroyanagi, Tomoki Hayashi, Kazuya Takeda, Tomoki Toda

the 30th European Signal Processing Conference (EUSIPCO) 2022.8

Emergence of Collaborative Hunting via Multi-Agent Deep Reinforcement Learning Reviewed

Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

International Workshop on Human Behavior Understanding (HBU'22) in conjunction with International Conference on Pattern Recognition (ICPR'22) 2022.8

Evaluating a third base coach’s decision-making via game theory and machine learning Reviewed

Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii

MathSport International, 2022 2022.7

Automatic screen-play classification in basketballvia semi-supervised learning Reviewed

Ziyi Zhang, Kazuya Takeda, Keisuke Fujii

Mathsport International 2022 2022.7

GNSS/IMU Performance Improvement Based on Acceleration Error Estimation Using Height Variation Reviewed

Aoki Takanose, Kaito Kondo, Yuta Hoda, Junichi Meguro, Kazuya Takeda

15th International Symposium on Advanced Vehicle Control (AVEC’22) Vol. Mo1B-01 2022.5

SecretSign: A Method of Finding a Specific Vehicle Privately and Quickly using Flashing Lights Reviewed

Yusuke Sakai, Hiromi Morita, Yoshio Ishiguro, Takanori Nishino, Kazuya Takeda

IEEE Intelligent Transportation Systems Magazine Vol. 14 ( 1 ) page： 216 - 227 2022.1

Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers

Naren Bao, Linda Capito, Dongfang Yang, Alexander Carballo, Chiyomi Miyajima, Kazuya Takeda

IEEE ACCESS Vol. 10 page： 36397 - 36415 2022

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

Most current research in the field of autonomous vehicle control assumes that all vehicles will follow the same patterns of automated driving behavior, resulting in systems with "conservative" or "average" driving styles. These systems may not be acceptable to drivers who prefer a more aggressive style of driving, however, while extremely cautious drivers may consider the standard outputs to be too aggressive. To address this problem, in this paper, we introduce Risk Sensitive Control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporates them into a receding-horizon controller. RSC uses a meta-learning algorithm to update the parameters of the cost function, continuously improving the controller online as more and more driving data is gathered from the user and subjective risk feedback. An estimator takes into account individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and its constraints. We test this approach using five lane change scenarios, some safe and some risky, with thirty real drivers in a CARLA simulation environment. Our quantitative and qualitative evaluations demonstrate that the proposed framework is able to generate a user's preferred driving maneuvers during lane changes, i.e., control commands the user associates with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user's own driving behavior.

DOI： 10.1109/ACCESS.2022.3163267

Web of Science

Real-to-Synthetic: Generating Simulator Friendly Traffic Scenes from Graph Representation

Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) page： 1615 - 1622 2022

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

Reproducing real-world traffic scenes in the simulator is fundamental to training self-driving systems. Creating a simulation scenario is a complex task, generally done manually: the ego-vehicle and other entities are placed and their trajectories dened, trying to recreate some situation found in real traffic. To reduce the manual burden, here we propose the Realto-Synthetic toolset. This toolset provides synthetic traffic scene in openDrive format, which can be directly simulated in many simulators such as SUMO or CARLA. Also, we provide a scene generator which generates near-realistic scene from minimum user effort. To maintain the similarity between real-world scene and generated one, here we introduce the concept "Road Scene Graph"(RSG). In this graph, nodes represent entities while edges stand for pairwise relationships. These relationships could be maintained in the scene generation process while the actor is generated according to the distribution sampled from realworld data. Experiments proved that by using "Road Scene Graph", our scene generator proposes a much more convenient way to configure traffic scenes rather than manually defining every actor's initial status and trajectories.

DOI： 10.1109/IV51971.2022.9827441

Web of Science

An enhanced driver's risk perception modeling based on gate recurrent unit network

Ping Peng, Ding Weiping, Liu Yongkang, Takeda Kazuya

2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) page： 1234 - 1240 2022

Auditory and visual warning information generation of the risk object in driving scenes based on weakly supervised learning

Niu Yinjie, Ding Ming, Zhang Yuxiao, Ohtani Kento, Takeda Kazuya

2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) page： 1572 - 1577 2022

Deep Reinforcement Learning in a Racket Sport for Player Evaluation With Technical and Tactical Contexts.

Ning Ding, Kazuya Takeda, Keisuke Fujii 0001

IEEE Access Vol. 10 page： 54764 - 54772 2022

Deepware: An Open-Source Toolkit for Developing and Evaluating Learning-Based and Model-Based Autonomous Driving Models

Shunya Seiya, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

IEEE ACCESS Vol. 10 page： 105734 - 105743 2022

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

In recent decades, many learning-based autonomous driving systems have been proposed, and researchers have also created toolkits for developing these systems. These toolkits allow developers to train their models easily, and then test them using simulators. Existing toolkits for learning-based autonomous driving systems have some limitations however, which include inability to reuse modules or to perform accurate comparisons with model-based systems, as well as a lack of support for middle-to-middle models. As a solution, in this paper we introduce Deepware, an end-to-end toolkit for developing and evaluating learning-based autonomous driving models. Deepware includes the tools needed for collecting and evaluating datasets, training models, and evaluating models on simulators or in real-world environments using actual vehicles. Unlike existing toolkits, we used ROS as our platform, which is a set of software frameworks for robot software development widely used in autonomous driving systems as middleware, which allows cooperation with model-based systems. This approach also allows system modules to be shared when building models. In addition, it allows the comparison of learning-based and model-based methods under the same conditions. Moreover, by extracting features from model-based systems, our toolkit can also support middle-to-middle models. The proposed Deepware toolkit and dataset are available at: https://github.com/shunchan0677/deepware.

DOI： 10.1109/ACCESS.2022.3212152

Web of Science

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Papers - TAKEDA, Kazuya