研究者詳細 - 奥田　裕之

論文 - 奥田　裕之

分割表示 123 件中 1 - 20 件目／全件表示 >>

An occlusion- and interaction-aware safe control strategy for autonomous vehicles 査読有り国際共著

Siddharth Gangadhar, Zhuoyuan Wang, Kofi Poku, Yorie Nakahira, Naoya Yamada, Kohei Honda, Hiroyuki Okuda, Tatsuya Suzuki

22nd IFAC World Congress 2023(IFAC WC2023) 頁： 8577 - 8584 2023年7月

MPC Builder for Autonomous Drive: Automatic Generation of MPCs for Motion Planning and Control 査読有り

Honda Kohei, Okuda Hiroyuki, Suzuki Tatsuya, Ito Akira

2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV 2023年6月

モデル予測制御による歩行者への配慮を意識した自動運転の設計査読有り

アルンムラリーダラン, 奥田裕之, 鈴木達也

計測自動制御学会論文集 59 巻 ( 11 ) 頁： 472 - 483 2023年

Configuration-aware model predictive motion planning for Tractor-Trailer Mobile Robot 査読有り

Ito Nobuaki, Okuda Hiroyuki, Suzuki Tatsuya

ADVANCED ROBOTICS 2022年9月

Development and evaluation of driving speed controller for lane merging considering surrounding driver’s intention toward stress-free driving 査読有り

14 巻 ( 1 ) 頁： 128 - 139 2021年6月

Quantitative Driver Acceptance Modeling for Merging Car at Highway Junction and Its Application to the Design of Merging Behavior Control 査読有り

Okuda Hiroyuki, Suzuki Tatsuya, Harada Kota, Saigo Shintaro, Inoue Satoshi

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22 巻 ( 1 ) 頁： 329 - 340 2021年1月

Parking Trajectory Planning Using Multiresolution State Roadmaps 査読有り国際誌

Yuichi Tazaki, Hiroyuki Okuda, Tatsuya Suzuki

IEEE Transactions on Intelligent Vehicles 2 巻 ( 4 ) 頁： 298-307 2017年12月

Modeling and Analysis of Driving Behavior based on Probability Weighted ARX Model 査読有り

Hiroyuki Okuda, Norimitsu Ikami, Tatsuya Suzuki, Yuichi Tazaki, Kazuya Takeda

IEEE Transactions on Intelligent Transportation Systems 14 巻 ( 1 ) 頁： 98-112 2012年8月

Analysis, Modeling, and Control of Merging Behavior at Low Speeds 査読有り国際誌

Tatsuya Ishiguro, Hiroyuki Okuda, Kenta Tominaga, Tatsuya Suzuki

2025 IEEE/SICE International Symposium on System Integration, SII 2025 頁： 782 - 788 2025年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：2025 IEEE/SICE International Symposium on System Integration, SII 2025

In a merging environment where automatic and manual operations are mixed, it is important for both parties to communicate with each other. While much research has been conducted at high speeds, such as on expressways, little progress has been made at low speeds, such as on ordinary roads. In this presentation, we will experimentally observe the driving characteristics of drivers in right-angle merging at low speeds, and construct a right-angle merging model that takes negotiation into account.

DOI： 10.1109/SII59315.2025.10871073

Scopus

Modeling of Cyclists' Decision for Left-Turn Vehicle at Unsignalized Intersection using Logistic Regression Model and Gaussian Mixuter Model 査読有り国際誌

Ryo Wakisaka, Takuma Yamaguchi, Kazunori Ban, Hiroyuki Okuda, Tatsuya Suzuki

2025 IEEE/SICE International Symposium on System Integration, SII 2025 頁： 789 - 796 2025年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：2025 IEEE/SICE International Symposium on System Integration, SII 2025

DOI： 10.1109/SII59315.2025.10870955

Inducibility Analysis of Pedestrian's Interactive Behavior 査読有り国際誌

Kentaro Sugiura, Hiroyuki Okuda, Mizuho Aoki, Kazuma Uchida, Tatsuya Suzuki

2025 IEEE/SICE International Symposium on System Integration, SII 2025 頁： 1 - 7 2025年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：2025 IEEE/SICE International Symposium on System Integration, SII 2025

As the application fields of Autonomous Mobile Robots (AMRs) expand, stress-free interaction with people around them becomes more important. AMRs are highly required to interact with pedestrians like human-human interaction. This paper proposes a new measure, 'Inducibility,' expressing how AMR's behavior can induce (control) the other pedestrian's behavior. In particular, this paper discusses two kinds of inducibility measures: one is based on the sensitivity of decision-making, and the other one is based on the controllability gramian for the state space model of the interactive behavior. Finally, the validity of these two measures is evaluated based on data on interaction behaviors, and typical applications of these measures are discussed.

DOI： 10.1109/SII59315.2025.10870962

Scopus

Switching Sampling Space of Model Predictive Path-Integral Controller to Balance Efficiency and Safety in 4WIDS Vehicle Navigation 査読有り

Mizuho Aoki, Kohei Honda, Hiroyuki Okuda, Tatsuya Suzuki

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS2024), 2024年10月

Comparative Study of Distributed Computation for Cooperative Adaptive Cruise Control using Model Prediction 査読有り

Hiroyuki Okuda, Toshiaki Hori, Hyuntai Chin, Tatsuya Suzuki

27th IEEE International Conference on Intelligent Transportation Systems (ITSC2024) 2024年9月

無信号交差点における歩行者の階層的横断判断モデル査読有り

西本宇志, 山口拓真, 奥田裕之, 鈴木達也, 脇坂龍, 伴和徳

計測自動制御学会論文集 60 巻 ( 9 ) 頁： 509 - 521 2024年9月

Operability evaluation of manual operation control for force-sensorless power-assist transport cart 査読有り国際誌

Ryuki Sato, Ryuga Nishida, Susumu Hara, Hiroyuki Okuda, Mitsuru Nagatsuka, Masahiko Tsuji, Tatsuya Suzuki

Mechatronics 100 巻 2024年6月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Mechatronics

In large-scale factories and warehouses, power-assisted carts are effective in transporting cargo to reduce the burden on workers. Herein, we developed a force-sensorless power-assisted cart system that can detect the operating force applied by the operator without using a force sensor and control the power-assisted drive. In this system, the operator directly grasps and operates the body of the cart, and the applied force is estimated from the cart body sway caused by the operation. The transport cart is controlled based on the estimated hand force. In such closed-loop systems formed by humans and machines, mechanical interactions affect the operability of the equipment. In this study, we experimentally verified the power-assisted manual operability of the proposed force-sensorless power-assisted transport cart. We examined the accuracy of the path tracking in manual operation experiments with 20 subjects and analyzed the results of the subjective evaluation. Based on the experimental results, the influence of the control parameters on operability was considered, and design guidelines were presented.

DOI： 10.1016/j.mechatronics.2024.103189

Scopus

Stein Variational Guided Model Predictive Path Integral Control: Proposal and Experiments with Fast Maneuvering Vehicles 査読有り国際誌

Honda K., Akai N., Suzuki K., Aoki M., Hosogaya H., Okuda H., Suzuki T.

Proceedings - IEEE International Conference on Robotics and Automation 29 巻頁： 7020 - 7026 2024年5月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Proceedings - IEEE International Conference on Robotics and Automation

This paper presents a novel Stochastic Optimal Control (SOC) method based on Model Predictive Path Integral control (MPPI), named Stein Variational Guided MPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal action distributions. While MPPI can find a Gaussian-approximated optimal action distribution in closed form, i.e., without iterative solution updates, it struggles with the mul-timodality of the optimal distributions. This is due to the less representative nature of the Gaussian. To overcome this limitation, our method aims to identify a target mode of the optimal distribution and guide the solution to converge to fit it. In the proposed method, the target mode is roughly estimated using a modified Stein Variational Gradient Descent (SVGD) method and embedded into the MPPI algorithm to find a closed-form "mode-seeking"solution that covers only the target mode, thus preserving the fast convergence property of MPPI. Our simulation and real-world experimental results demonstrate that SVG-MPPI outperforms both the original MPPI and other state-of-the-art sampling-based SOC algorithms in terms of path-tracking and obstacle-avoidance capabilities. https://github.com/kohonda/proj-svg-mppi

DOI： 10.1109/icra57147.2024.10611021

Scopus

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無信号交差点での順行左折車両に対するサイクリストの行動意図の分析とモデル化 Open Access

脇坂龍, 山口拓真, 伴和徳, 奥田裕之, 鈴木達也

自動車技術会論文集 55 巻 ( 5 ) 頁： 991 - 998 2024年5月

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：公益社団法人自動車技術会

自動運転システムの評価をシミュレーションによって行うには，交通参加者のモデル化が必要である．特に自転車はルール認識の曖昧さなどにより不規則な動きをするため安全面で重要な要因となる．本研究では，シミュレータでの計測データを元に順行左折車に対するサイクリスト行動の分析，モデル化について検討した．

DOI： 10.11351/jsaeronbun.55.991

Open Access

CiNii Research

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Evaluation of Controllability of Interaction Between Pedestrian and Autonomous Mobile Robot in Shared Mobility Space 査読有り

Kentaro Sugiura, Mizuho Aoki, Kazuhide Kuroda, Hiroyuki Okuda, Tatsuya Suzuki

Proceedings of the International Conference on Informatics in Control, Automation and Robotics 頁： 249 - 257 2023年11月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Proceedings of the International Conference on Informatics in Control, Automation and Robotics

Recently, a growing number of autonomous mobile robots (AMR) coexisting with humans are being introduced in many types of AMR-human shared space. Such AMR often needs to be navigated in narrow spaces while smoothly interacting with pedestrians. In such a situation, AMRs are highly recommended to estimate the pedestrian’s intentions and take appropriate action from the viewpoint of social acceptance. First, this paper presents new modeling and understanding of pedestrian behavior, particularly focusing on decision-making when they face an AMR at a close distance. Real-world experiments were conducted using a remote switch to directly record their decisions, and a mathematical decision model is made by using a logistic regression model. In the interaction between AMR and pedestrians, the AMR is expected to ‘implicitly control’ the interacting pedestrian by changing its own action. From this perspective, the influence of the AMR motion on the pedestrian’s decision is formally defined and calculated by using the controllability Gramian of the augmented AMR-pedestrian system model. A deep understanding of the influence of AMR action on pedestrian behavior will be beneficial to develop control policies for smooth AMR-pedestrian interactions.

DOI： 10.5220/0012177500003543

Scopus

Bayesian Network Model of Decision-Making for Pedestrians' Crossing Behavior Considering Gaze Information at Unsignalized Intersection 査読有り

Takashi Nishimoto, Takuma Yamaguchi, Hiroyuki Okuda, Tatsuya Suzuki 0001, Ryo Wakisaka, Kazunori Ban

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 頁： 4408 - 4415 2023年9月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Understanding pedestrians' behavior is a crucial challenge for achieving fully autonomous driving because it has a high degree of freedom and varies from person to person. This paper particularly focuses on the decision-making of the pedestrian, and aims to build a mathematical model of it. Generally, pedestrians make decisions considering other traffic participants and the traffic environment. Consequently, in a complex situation with multiple traffic participants, the mechanism of decision-making becomes intricate, and this makes it difficult to obtain an accurate mathematical model. To address this issue, this paper proposes a structured probabilistic model for the decision-making considering gaze information. In the proposed model, pedestrians' overall decision-making is divided into individual decision-making for each traffic participant considering gaze information, and expressed by a logistic regression model. These divided decision-makings are then integrated using a Bayesian network. By introducing this structure for the decision-making, the expansion of the state space of the model can be suppressed, and a reduction of the amount of training data can be achieved. Finally, to validate the proposed method, simulator experiments are conducted focusing on the crossing behavior at unsignalized intersection, and the performance of the model is confirmed.

DOI： 10.1109/ITSC57777.2023.10421888

Scopus

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その他リンク： https://dblp.uni-trier.de/db/conf/itsc/itsc2023.html#NishimotoYO0WB23

Bayesian Network-Based Probabilistic Constraints for Safe Autonomous Driving in Occlusion Environments 査読有り国際共著

Naoya Yamada, Kohei Honda, Hiroyuki Okuda, Tatsuya Suzuki, Yorie Nakahira

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 頁： 3770 - 3777 2023年9月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

This paper considers how to realize safe autonomous driving against hazards that lurk in occlusion, such as pedestrians darting out. Occlusion is a potential risk factor that cannot be directly detected by sensors and is one of the critical issues that need to be addressed to realize safe autonomous driving. However, if all dangerous scenarios resulting from occlusion are considered, the system may become overly conservative. One of the promising control schemes to guarantee the safety of traffic participants who dart out from the occlusion is based on a probabilistic framework that always maintains the safety of the vehicle with a certain probability. Although the control including probabilistic constraints achieves the control that is not too conservative, it has not been fully discussed how to derive reasonable probabilistic constraints for unknown occlusion environments. In addition, the computational complexity to calculate the probability must be reduced in order to be exploited for real-time control. Therefore, this paper proposes a method to calculate probability constraints for occlusion hazards based on a Bayesian network. In the proposed Bayesian network, prior information including the driving environment and features of occlusion are considered naturally. This property will not only drastically simplify the analysis of failure behaviors but also enable transparency in the exposed risks. The proposed method is confirmed to be computationally less expensive than the Monte Carlo method, and the usefulness of the control using the proposed probability constraints is verified by simulation.

DOI： 10.1109/ITSC57777.2023.10422408

Scopus

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