Faculty Profiles - TAKEDA, Kazuya

Papers - TAKEDA, Kazuya

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Pitching strategy evaluation via stratified analysis using propensity score Reviewed

Nakahara Hiroshi, Takeda Kazuya, Fujii Keisuke

JOURNAL OF QUANTITATIVE ANALYSIS IN SPORTS 2023.5

Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

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

JOURNAL OF ROBOTICS AND MECHATRONICS Vol. 35 ( 2 ) page： 387 - 397 2023.4

Multi-Agent Deep-Learning Based Comparative Analysis of Team Sport Trajectories Reviewed

Zhang Ziyi, Bunker Rory, Takeda Kazuya, Fujii Keisuke

IEEE ACCESS Vol. 11 page： 43305 - 43315 2023

Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles

Manibardo Eric L., Lana Ibai, Del Ser Javier, Carballo Alexander, Takeda Kazuya

2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV 2023

Synthesizing Realistic Snow Effects in Driving Images Using GANs and Real Data with Semantic Guidance

Yang Hanting, Ding Ming, Carballo Alexander, Zhang Yuxiao, Ohtani Kento, Niu Yinjie, Ge Maoning, Feng Yan, Takeda Kazuya

2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV 2023

Open-world driving scene segmentation via multi-stage and multi-modality fusion of vision-language embedding Reviewed

Niu Yingjie, Ding Ming, Zhang Yuxiao, Ge Maoning, Yang Hanting, Takeda Kazuya

2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV 2023

LiDAR Point Cloud Translation Between Snow and Clear Conditions Using Depth Images and GANs Reviewed

Zhang Yuxiao, Ding Ming, Yang Hanting, Niu Yingjie, Feng Yan, Ge Maoning, Carballo Alexander, Takeda Kazuya

2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV 2023

Action Valuation of On- and Off-Ball Soccer Players Based on Multi-Agent Deep Reinforcement Learning

Nakahara, H; Tsutsui, K; Takeda, K; Fujii, K

IEEE ACCESS Vol. 11 page： 131237 - 131244 2023

Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs

Tanaka, R; Suzuki, T; Takeda, K; Fujii, K

PROCEEDINGS OF THE 6TH INTERNATIONAL WORKSHOP ON MULTIMEDIA CONTENT ANALYSIS IN SPORTS, MMSPORTS 2023 page： 41 - 48 2023

Personalized Causal Factor Generalization for Subjective Risky Scene Understanding with Vision Transformer

Bao, N; Carballo, A; Tsukada, M; Takeda, K

2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC page： 4637 - 4643 2023

Recognition Assistance Interface for Human-Automation Cooperation in Pedestrian Risk Prediction

Kuribayashi, A; Takeuchi, E; Carballo, A; Ishiguro, Y; Takeda, K

SAE INTERNATIONAL JOURNAL OF CONNECTED AND AUTOMATED VEHICLES Vol. 6 ( 3 ) 2023

Learning to Predict Navigational Patterns from Partial Observations Reviewed

Robin Karlsson, Alexander Carballo, Francisco Lepe-Salazar, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

IEEE Robotics and Automation Letters Vol. 8 ( 9 ) page： 5592 - 5599 2023.9

Publishing type：Research paper (scientific journal)

Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enable our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception.

DOI： 10.1109/LRA.2023.3291924

Web of Science

Scopus

Personalized lane changes using subjective risk-sensitive framework

Naren Bao, Alexander Carballo, Kazuya Takeda

Towards Human-Vehicle Harmonization page： 211 - 222 2023.3

Publishing type：Part of collection (book)

Most of the current researches on autonomous vehicles' control assume that all vehicles should have the same patterns of driving implemented, resulting in conservative or average systems. However, these results may not be acceptable to drivers who prefer a more aggressive style of driving, while extremely cautious drivers may consider the standard outputs to be too aggressive. In this chapter, we introduce risk-sensitive control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporate 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 for subjective risk feedback. The estimator takes into account the individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and constraints. We test this approach using five-lane change scenarios, some safe and some risky, with 30 real drivers in a CARLA simulation environment. Based on both quantitative and qualitative evaluations, our experimental results demonstrate that the proposed framework can generate users' preferred driving commands during lane changes, that is, commands associated with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user's own driving behavior.

DOI： 10.1515/9783110981223-016

Scopus

Framework for Generation and Removal of Multiple Types of Adverse Weather from Driving Scene Images Reviewed

Hanting Yang, Alexander Carballo, Yuxiao Zhang, Kazuya Takeda

SENSORS Vol. 23 ( 3 ) page： 1548 2023.2

Language：English Publishing type：Research paper (scientific journal) Publisher：MDPI

Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to improve the quality of input images are two promising solutions. In this work, we propose Multiple Weather Translation GAN (MWTG), a CycleGAN-based, dual-purpose framework that simultaneously learns weather generation and its removal from image data. MWTG consists of four GANs constrained using cycle consistency that carry out domain translation tasks between hazy, rainy, snowy, and clear weather, using an asymmetric approach. To increase network capacity, we employ a spatial feature transform (SFT) layer to fuse the features extracted from the weather layer, which contains high-level domain information from the previous generators. Further, we collect an unpaired, real-world driving dataset recorded under various weather conditions called Realistic Driving Scenes under Bad Weather (RDSBW). We qualitatively and quantitatively evaluate MWTG using the RDSBW and the variation of Cityscapes that synthesize weather effects, eg., FoggyCityscape. Our experimental results suggest that MWTG can generate realistic weather in clear images and also accurately remove noise from weather images. Furthermore, the SOTA pedestrian detector ASCP is shown to achieve an impressive gain in detection precision after image restoration using the proposed MWTG method.

DOI： 10.3390/s23031548

Web of Science

Estimating the effect of hitting strategies in baseball using counterfactual virtual simulation with deep learning Reviewed

Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii

International Journal of Computer Science in Sport Vol. 22 ( 1 ) page： 1 - 12 2023.1

Perception and sensing for autonomous vehicles under adverse weather conditions: A survey Reviewed

Yuxiao Zhang, Alexander Carballo, Hanting Yang, Kazuya Takeda

ISPRS Journal of Photogrammetry and Remote Sensing Vol. 196 page： 146 - 177 2023.1

RSG-GCN: Predicting Semantic Relationships in Urban Traffic Scene With Map Geometric Prior

Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS Vol. 4 page： 244 - 260 2023

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

Automated identification of the relationships between traffic actors and surrounding objects, in order to describe their behavior and predict their intentions, has become the focus of increasing attention in the field of autonomous driving. Therefore, in this work, we propose a Road Scene Graphs-Graph Convolutional Network (RSG-GCN) as a novel, graph-based model for predicting the topological graph structure of a given traffic scene. The status of the actors and HD map information are integrated as prior knowledge, allowing the edges linking the actor nodes to capture potential semantic relationships, such as "vehicle approaching pedestrian" and "pedestrian waiting at intersection". To train this model, we created our own RSG dataset, as well as a relational dataset and benchmark derived from nuScenes. Our extensive range of experiments demonstrate that our model can more accurately predict semantic relationships and behavior in a given traffic scene than other popular traffic scene prediction models. In particular, regarding the use of HD map prior knowledge, we found that the resulting increase in accuracy significantly outweighs performance loss caused by the increase in graph size. The downstream applications of RSG include traffic scene retrieval and synthetic traffic scene generation, which are briefly described.

DOI： 10.1109/OJITS.2023.3260624

Web of Science

Real-Time Graph-Based Optimization for GNSS-Doppler Integrated RTK-GNSS/IμDR Positioning System in Urban Area

Aoki Takanose, Eijiro Takeuchi, Alexander Carballo, Junichi Meguro, Kazuya Takeda

IEEE Intelligent Vehicles Symposium, Proceedings Vol. 2023-June 2023

Publishing type：Research paper (international conference proceedings)

Autonomous driving of vehicles and robots requires highly accurate position information, and RTK-GNSS is expected to be utilized for this purpose. In this paper, we propose a robust and real-time operation method by introducing graph optimization into the integrated RTK-GNSS/IMU method. The proposed method is an extension of a method using vehicle trajectories that can estimate positions with lane-level accuracy even in urban areas. The position is estimated by removing GNSS multipaths from the shape of a vehicle trajectory of several hundred meters and averaging the remaining GNSS results. This method does not take into account the errors in the vehicle trajectory and cannot fully benefit from the high accuracy positioning solution of RTK-GNSS. To solve this problem, we introduce graph optimization to the base method, which treats the error state as a probabilistic model. However, general graph optimization methods have problems with processing time and outlier elimination. The proposed method solves these problems by restricting the time series data to be optimized and using a two-step optimization structure. Evaluations show that the proposed method is effective because it satisfies the requirements for real-time operation and improves accuracy compared to conventional methods.

DOI： 10.1109/IV55152.2023.10186672

Web of Science

Scopus

Uncertainty Aware Task Allocation for Human-Automation Cooperative Recognition in Autonomous Driving Systems

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

IEEE Intelligent Vehicles Symposium, Proceedings Vol. 2023-June 2023

Publishing type：Research paper (international conference proceedings)

Cooperative recognition, a method to achieve human-automation cooperation in the recognition phase of the autonomous driving system, has been proposed to address the challenges in the conventional control phase cooperation, e.g., taking over vehicle control. In cooperative recognition, the operator intervenes in recognition tasks that are difficult for the automated system alone to improve driving efficiency and safety. The challenge is the integration of both human and automated systems while both participants have different characteristics, processing capabilities, and uncertainty in the decisions (recognition results). The objectives of this study are task allocation (i.e., when and for which targets the operator should intervene) taking into account the intervention efficiency and human state. And also combine the human intervention and recognition result of the automated systems to solve the uncertainties in both participants. We formulated this problem with a Partially Observable Markov Decision Process (POMDP). The simulator experiment indicated that the recognition result of the automated system and the operator's intervention were stochastically combined. The intervention requests to the operator adapted to the operator state and could be reduced while maintaining driving efficiency and minimizing risk omissions.

DOI： 10.1109/IV55152.2023.10186725

Web of Science

Scopus

RSG-Search: Semantic Traffic Scene Retrieval Using Graph-Based Scene Representation

Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

IEEE Intelligent Vehicles Symposium, Proceedings Vol. 2023-June 2023

Publishing type：Research paper (international conference proceedings)

Browsing specific traffic scene in large-scale dataset is an increasing demand from researchers, self-driving community and insurance companies. It is easy to search scenes with specific tags such as "rain", "snow", or "on highway". However, searching specific scene configurations, like "two vehicles waiting for a person crossing the road", is still an open problem. In this paper, we provide RSG-search, a scene-graph based traffic scene retrieval method, based on our previous research on traffic scene-graph generation. By previously translating open datasets to scene graphs, we can ignore irrelevant details, and efficiently search specific scene configuration among thousands of traffic scenes. Experiment results shows that our graph searching method is able to retrieve results for a given query with high accuracy. Our method simplifies the task of scene retrieval, opening opportunities for new applications.

DOI： 10.1109/IV55152.2023.10186641

Web of Science

Scopus

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Papers - TAKEDA, Kazuya