Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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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

Disentangled Bad Weather Removal GAN for Pedestrian Detection

Yang Hanting, Carballo Alexander, Takeda Kazuya

2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING) 2022

Driving Risk and Intervention: Subjective Risk Lane Change Dataset

Naren Bao, Alexander Carballo, Kazuya Takeda

2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) page： 556 - 562 2022

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

When developing truly driverless mobility for the future, one key index used to measure the matureness of a particular self-driving technology is the driver intervention rate. One method which has proven to be effective for decreasing intervention rates is the use of personalized driving models that can mimic the driving style and preferences of a targeted user, so that autonomous driving feels safer and more natural to them. To create such models, quantitative data should be collected from users in order to determine the style of driving that a particular user, or type of user, prefers. In this paper, we introduce the Subjective Risk Lane Change (SRLC) Dataset, which includes ego vehicle driving behavior data, surrounding vehicle location information, and the subjective risk scores of users, collected during both safe and risky lane change scenarios encountered in CARLA simulators, as well as demographic information for our 30 participants. Furthermore, user intervention data for all of our participants was collected from Personalized Model Predictive Controllers during the generated lane change maneuvers. As far as the authors are able to determine, no other public dataset provides driving behavior signal and intervention timing information collected during driver interventions. Our dataset can be used to gain insights into a variety of personal driving styles, allowing the improvement of adaptive autonomous driving systems, and leading to safer and more widely accepted driverless technology.

DOI： 10.1109/IV51971.2022.9827358

Web of Science

Occlusion-Aware Motion Planning With Visibility Maximization via Active Lateral Position Adjustment

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

IEEE ACCESS Vol. 10 page： 57759 - 57782 2022

Estimating counterfactual treatment outcomes over time in multi-vehicle simulation. Reviewed

Keisuke Fujii 0001, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

SIGSPATIAL/GIS ( 7 ) page： 7 - 4 2022

Cooperative play classification in team sports via semi-supervised learning. Reviewed

Zhang Ziyi, Kazuya Takeda, Keisuke Fujii

International Journal of Computer Science in Sport Vol. 21 ( 1 ) page： 111 - 121 2022

Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment.

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

BMVC page： 699 - 699 2022

Autonomous Driving in Adverse Weather Conditions: A Survey Reviewed

Y. Zhang, A. Carballo, H. Yang, K. Takeda

arXiv preprint arXiv:2112.08936 2021.12

ViCE: Self-Supervised Visual Concept Embeddings as Contextual and Pixel Appearance Invariant Semantic Representations Reviewed

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

arXiv preprint arXiv:2111.12460 2021.11

AN ENSEMBLE APPROACH TO ANOMALOUS SOUND DETECTION BASED ON CONFORMER-BASED AUTOENCODER AND BINARY CLASSIFIER INCORPORATED WITH METRIC LEARNING

Ibuki Kuroyanagi , Tomoki Hayashi , Yusuke Adachi , Takenori Yoshimura , Kazuya Takeda , Tomoki Toda

Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021) page： 110 - 114 2021.11

End-to-End Learning-based Driving System with Branches by Emphasizing Target Direction Reviewed

Seiya, Shunya; Ohtani, Kento; Carballo, Alexander; Takeuchi, Eijiro; Takeda, Kazuya;

Vol. 52 ( 6 ) 2021.11

FollowSelect: Path-based Menu Interaction for Intuitive Navigation Reviewed

Vol. 62 ( 10 ) page： 1669 - 1680 2021.10

Flexible prediction of opponent motion with internal representation in interception behavior Reviewed

Kazushi Tsutsui, Keisuke Fujii, Kazutoshi Kudo, Kazuya Takeda

Biological Cybernetics Vol. 115 ( 5 ) page： 473 - 485 2021.10

Authorship：Last author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Springer Science and Business Media LLC

<title>Abstract</title>Skilled interception behavior often relies on accurate predictions of external objects because of a large delay in our sensorimotor systems. To deal with the sensorimotor delay, the brain predicts future states of the target based on the current state available, but it is still debated whether internal representations acquired from prior experience are used as well. Here we estimated the predictive manner by analyzing the response behavior of a pursuer to a sudden directional change of the evasive target, providing strong evidence that prediction of target motion by the pursuer was incompatible with a linear extrapolation based solely on the current state of the target. Moreover, using neural network models, we validated that nonlinear extrapolation as estimated was computationally feasible and useful even against unknown opponents. These results support the use of internal representations in predicting target motion, suggesting the usefulness and versatility of predicting external object motion through internal representations.

DOI： 10.1007/s00422-021-00891-9

Web of Science

Other Link： https://link.springer.com/article/10.1007/s00422-021-00891-9/fulltext.html

Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union Reviewed

Rory Bunker, Keisuke Fujii, Hiroyuki Hanada, Ichiro Takeuchi

PLOS One 2021.9

Anomalous Sound Detection Using a Binary Classification Model and Class Centroids Reviewed

Ibuki Kuroyanagi, Tomoki Hayashi, Kazuya Takeda , Tomoki Toda

European Signal Processing Conference2021(EUSIPCO2021) 2021.8

Prediction of Personalized Driving Behaviors via Driver-Adaptive Deep Generative Models Reviewed

Naren Bao, Alexander Carballo, Takeda Kazuya

2021 IEEE Intelligent Vehicles Symposium (IV) page： 616 - 621 2021.7

ANOMALOUS SOUND DETECTION WITH ENSEMBLE OF AUTOENCODER AND BINARY CLASSIFICATION APPROACHES

Ibuki Kuroyanagi , Tomoki Hayashi , Yusuke Adachi, , Takenori Yoshimura , Kazuya Takeda , Tomoki Toda

Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021) 2021.7

Leveraging state-of-the-art ASR techniques to audio captioning Reviewed

Narisetty, Chaitanya; Hayashi, Tomoki; Ishizaki, Ryunosuke; Watanabe, Shinji; Takeda, Kazuya;

Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021) page： 160 - 164 2021.7

Deadlock-free planner for occluded intersections using estimated visibility of hidden vehicles Reviewed

Patiphon Narksri, Eijiro Takeuchi, Yoshiki Ninomiya, Kazuya Takeda

Electronics (Switzerland) Vol. 10 ( 4 ) page： 1 - 28 2021.2

Language：Japanese Publishing type：Research paper (scientific journal)

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility.

DOI： 10.3390/electronics10040411

Web of Science

Scopus

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Papers - TAKEDA, Kazuya