研究者詳細 - 奥田　裕之

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

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

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

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

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

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

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

Analysis of Smartphone Walking Behavior of Pedestrians at Unsignalized Intersection Based on Decisions and Motion 査読有り

Yuki Ban, Takuma Yamaguchi, Hiroyuki Okuda, Tatsuya Suzuki, Chieko Nishizawa, Kazunori Ban

2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023 頁： 1184 - 1189 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：2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023

In recent years, smartphone walking has become a hot topic of discussion as a dangerous behavior of pedestrians, and understanding these behaviors is a crucial issue in reducing traffic accidents. However, analysis for smartphone walking targets only human cognition, and pedestrian decisions and motion have not been well analyzed. Therefore, in this paper, pedestrian simulators were constructed using a VR device to measure pedestrian decisions and motion. The pedestrian's decision is represented by a logistic regression model, and the ambiguity of the decision is evaluated by information entropy based on this model. In addition, the pedestrian's motion was analyzed based on statistics to investigate the impact of smartphone walking on their decisions and motion.

DOI： 10.23919/SICE59929.2023.10354222

How impressions of other drivers affect one's behavior when merging lanes

Actual-Vehicle Verification of Automatic Parking in Narrow Spaces Considering Precise Collision Avoidance Constraints for Rectangular Objects 査読有り

Koki Hoshino , Kohei Honda , Mizuho Aoki , Hiroyuki Okuda, and Tatsuya Suzuki

2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023 頁： 646 - 651 2023年9月

Observation and Modeling of Decision-Making of Pedestrian with Interactions at X-Crossing 査読有り

Kazuma Uchida, Naoki Kodama, Hiroyuki Okuda, Kazuhide Kuroda, Tatsuya Suzuki

2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023 頁： 1184 - 1189 2023年9月

Combination of Reinforcement Learning Models Towards Considerate Motion Planning for Multiple Pedestrians 査読有り

Neel Mishra, Takuma Yamaguchi, Hiroyuki Okuda, Tatsuya Suzuki

22nd IFAC World Congress 2023(IFAC WC2023) 頁： 3992 - 3997 2023年7月

Longitudinal and Turning Manual Operation Control for a Force Sensorless Power-Assisted Transport Cart 査読有り

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

22nd IFAC World Congress 2023(IFAC WC2023) 頁： 1189 - 1194 2023年7月

Multi-Horizon and Multi-Rate Model Predictive Control for Integrated Longitudinal and Lateral Vehicle Control 査読有り

Kuan Ching Lin, Honda Kohei, Okuda Hiroyuki, Suzuki Tatsuya

2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV 2023年6月

Connection of nonlinear model predictive controllers for smooth task switching in autonomous driving 査読有り

Honda Kohei, Okuda Hiroyuki, Suzuki Tatsuya, Ito Akira

ASIAN JOURNAL OF CONTROL 25 巻 ( 3 ) 頁： 1805 - 1822 2023年5月

Adaptive Mode-switching From Autonomous Driving Mode to Manual Operation Mode of Mobile Robot Based on Body Sway 査読有り

Ryuki Sato, Shoya Hirokawa, Susumu Hara, Ryuga Nishida, Hiroyuki Okuda,Mitsuru Nagatsuka, Tatsuya Suzuki

システム制御情報学会論文誌「フレキシブル・オートメーション」総合特集号 36 巻 ( 3 ) 頁： 55 - 63 2023年3月

Analysis and Modeling of Traffic Participants Considering Interactions at Intersections Without Traffic Signals 査読有り

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

2023 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION, SII 2023年1月

Obstacle Avoidance Control Based on Nonlinear MPC for All Wheel Driven In-Wheel EV in Steering Failure 査読有り

Mizuho Aoki, Kohei Honda, Hiroyuki Okuda, Tatsuya Suzuki, Akira Ito, Daisuke Nagasaka

2022 IEEE 25th International Conference on Intelligent Transportation Systems 2022年9月

Indication of interaction plans based on model predictive interaction control: Cooperation between AMRs and pedestrians using eHMI 査読有り

Kosuke Suzuki, Takuma Yamaguchi, Hiroyuki Okuda, Tatsuya Suzuki

SICE Annual Conference 2022 頁： 1232 - 1237 2022年9月

Evaluation of Impact by Control Switching According to Driving Environment 査読有り

Yusuke Oi, Takuma Yamaguchi, Hiroyuki Okuda, Tatsuya Suzuki

SICE Annual Conference 2022 頁： 521 - 526 2022年9月

Design of an Autonomous driving system based on pedestrian emotions using Human-in-the-Loop Reinforcement Learning

Neel Mishra, Hiroyuki Okuda, Tatsuya Suzuki

SICE Annual Conference 2022 頁： 761 - 765 2022年9月

Shimojo A., Ninomiya Y., Miwa K., Terai H., Matsubayashi S., Okuda H., Suzuki T.

Transportation Research Part F: Traffic Psychology and Behaviour 89 巻頁： 236 - 248 2022年8月

掲載種別：研究論文（学術雑誌）出版者・発行元：Transportation Research Part F: Traffic Psychology and Behaviour

In recent years, systems have been developed to realize automatic driving based on objective information such as the relative distance and relative speed between vehicles. However, humans still must drive in complex situations, for instance, when merging lanes. In such driving situations, it is possible that people make decisions based not only on objective information, but also on subjective information. This study examined how subjective information, specifically, a driver's impression of the other vehicle, affects the decision to merge in front of or behind the other vehicle when merging lanes on a highway. Twenty participants (nmale = 10; nfemale = 10; Mage = 43.92 [SDage = 11.40]) joined two experiments, Days 1E and 2E, using a driving simulator. Two months after participating in Day 1E the participants joined Day 2E. In the Day 1E, they drove either on the merging lane or the main lane and merged lanes while considering the other vehicle driving along the adjacent lane. This experiment measured the probability that the participants drove in front of another vehicle upon merging, which is defined as “lead probability.” The Day 2E was similar to 1E, except for the manipulation of the participants’ impression of the other vehicle as being aggressive/cautious via acceleration/deceleration of the other vehicle, and through the contents of the instructions regarding the other vehicle's driving characteristics. In the Day 2E, the participants were randomly assigned to two: Aggressive or Cautious conditions. As the result of comparing the lead probabilities, it was found that only when the participants were driving on the merging lane and had the impression that the other vehicle is aggressive, the impression lowered the lead probability. The result indicates that people make decisions based not only on objective information but also on subjective information for specific driving situations, such as merging lanes. These findings can help in the development of automated driving systems that allow safer merging.

DOI： 10.1016/j.trf.2022.06.007

確率的な時変パラメータをもつ制御器モデルによる車線維持行動のモデル化

モデル予測型経路追従制御における車両運動予測モデルの比較検証査読有り

青木瑞穂, 本田康平, 奥田裕之, 鈴木達也

自動車技術会論文集 53 巻 ( 3 ) 頁： 687 - 692 2022年5月

Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving 査読有り

Muraleedharan Arun, Okuda Hiroyuki, Suzuki Tatsuya

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 7 巻 ( 1 ) 頁： 11 - 20 2022年3月

Manual Control Mode for Autonomous Mobile Robots Using Velocity-Based Impedance Control

Thao Nguyen Gia Minh, Hara Susumu, Hirokawa Shoya, Okuda Hiroyuki, Nagatsuka Mitsuru, Ogawa Naoki, Suzuki Tatsuya

IEEJ JOURNAL OF INDUSTRY APPLICATIONS 11 巻 ( 5 ) 頁： 650 - 663 2022年

Model Predictive Collision Avoidance for Non-Convex Environment Using Projected C-Space 査読有り

Ishiguro Tatsuya, Yamaguchi Takuma, Okuda Hiroyuki, Suzuki Tatsuya

2022 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII 2022) 頁： 525 - 530 2022年

Modelling and Analysis for Interactive Crossing Decision of Pedestrian at Non-signalized Intersection 査読有り

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

2022 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII 2022) 頁： 900 - 907 2022年

確率的な時変パラメータを持つ制御器モデルによる車線維持行動のモデル化査読有り

辻悠介，菅本周作，奥田裕之，山口拓真，鈴木達也

計測自動制御学会論文誌 58 巻 ( 12 ) 2022年

Indication of interaction plans based on model predictive interaction control: Cooperation between AMRs and pedestrians using eHMI

Suzuki Kosuke, Yamaguchi Takuma, Okuda Hiroyuki, Suzuki Tatsuya

2022 61ST ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS (SICE) 頁： 1232 - 1237 2022年

Evaluation of impact by control switching according to driving environment

Oi Yusuke, Yamaguchi Takuma, Okuda Hiroyuki, Suzuki Tatsuya

2022 61ST ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS (SICE) 頁： 521 - 526 2022年

辻悠介, 菅本周作, 奥田裕之, 山口拓真, 鈴木達也

計測自動制御学会論文集 58 巻 ( 12 ) 頁： 581 - 590 2022年

Configuration-aware Model Predictive Motion Planning in Narrow Environment for Autonomous Tractor-trailer Vehicle 査読有り

Nobuaki Ito, Hiroyuki Okuda, Shinkichi Inagaki, Tatsuya Suzuki

Proceedings of 47th Annual Conference of the IEEE Industrial Electronics Society 頁： 1 - 6 2021年10月

Variable Selection and Modeling of Drivers' Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information 査読有り

Nwadiuto Jude C., Yoshino Soichi, Okuda Hiroyuki, Suzuki Tatsuya

IEEE ACCESS 9 巻頁： 127672 - 127684 2021年9月

受け入れられる運転知能を目指して―人の行動予測を用いた低ストレスな協調制御査読有り

奥田裕之・山口拓真

システム／制御／情報 65 巻 ( 9 ) 頁： 370 - 376 2021年9月

Introduction of a Manual Control Mode for Autonomous Mobile Robots to Transportation of Equipment in Manufacturing Sites of Aircraft Industry

Gia Minh Thao Nguyen, Shoya Hirokawa, Susumu Hara, Hiroyuki Okuda, Mitsuru Nagatsuka, Naoki Ogawa, Tatsuya Suzuki

2021 IEEE International Conference on Advanced Robotics and its Social Impacts 頁： 127 - 130 2021年7月

Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model 査読有り

Nwadiuto Jude Chibuike, Okuda Hiroyuki, Suzuki Tatsuya

APPLIED SCIENCES-BASEL 11 巻 ( 11 ) 2021年6月

Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance 査読有り

Chen Keji, Yamaguchi Takuma, Okuda Hiroyuki, Suzuki Tatsuya, Guo Xuexun

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22 巻 ( 5 ) 頁： 2751 - 2760 2021年5月

Model Predictive Path Planning for Autonomous Parking Based on Projected C-Space 査読有り

Yamaguchi Takuma, Ishiguro Tatsuya, Okuda Hiroyuki, Suzuki Tatsuya

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) 頁： 929 - 935 2021年

Design of Fail-safe Model Predictive Controller for Sudden Changes in Driving Scenes 査読有り

Bui Ha, Muraleedharan Arun, Sinha Srishti, Okuda Hiroyuki, Suzuki Tatsuya

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) 頁： 790 - 797 2021年

Comparative Study of Prediction Models for Model Predictive Path-Tracking Control in Wide Driving Speed Range 査読有り

Aoki Mizuho, Honda Kohei, Okuda Hiroyuki, Suzuki Tatsuya

2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) 頁： 1261 - 1267 2021年

Configuration-aware Model Predictive Motion Planning in Narrow Environment for Autonomous Tractor-trailer Mobile Robot

Ito Nobuaki, Okuda Hiroyuki, Inagaki Shinkichi, Suzuki Tatsuya

IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY 2021年

受け入れられる運転知能を目指して : 人の行動予測を用いた低ストレスな協調制御—「人と繋がる制御システムの未来をのぞく」特集号

65 巻 ( 9 ) 頁： 370 - 376 2021年

非線形モデル予測制御を用いたレーンチェンジにおける行動決定・経路計画・制御の同時実行査読有り

本田康平, 奥田裕之, 鈴木達也

自動車技術会論文集 52 巻 ( 1 ) 頁： 100 - 105 2020年12月

Simultaneous Realization of Decision, Planning and Control for Lane-Changing Behavior Using Nonlinear Model Predictive Control

Okuda Hiroyuki, Sugie Nobuto, Suzuki Tatsuya, Haraguchi Kentaro, Kang Zibo

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E103D 巻 ( 12 ) 頁： 2632 - 2642 2020年12月

A hierarchical hybrid system of integrated longitudinal and lateral control for intelligent vehicles 査読有り

Chen, KJ; Pei, XF; Okuda, H; Zhu, ML; Guo, XX; Guo, KH; Suzuki, T

ISA TRANSACTIONS 106 巻頁： 200 - 212 2020年11月

記述言語：日本語出版者・発行元：ISA Transactions

A hierarchical hybrid control system is proposed to cope with highly automated driving in highway environments with multiple lanes and surrounding vehicles. In the high-level layer, the discrete driving decisions are coordinated by the finite-state machine (FSM) based on the relative position identification and predictive longitudinal distance of the surrounding vehicles. The low-level layer is responsible for the vehicle motion control, where the model predictive control (MPC) approach is utilized to integrate the longitudinal and lateral control mainly including car-following control and lane changing control. The proposed control system focuses on two issues regarding safe driving on highways. On one hand, the subject vehicle must always keep a safe distance with its leading vehicle to avoid the rear-end collision. On the other hand, the subject vehicle should also overtake the preceding vehicle by safe lane changes if the desired speed is not achieved. The effectiveness of the hybrid control is tested in the simulation, whose results verify that the driving decisions are made reasonably and the vehicle motion control obeys stability and comfort requirements. Moreover, it is also indicated by the simulations in random scenarios that the control strategy is able to deal with most of ordinary situations on highways although some emergency situations or critical driving maneuvers of other vehicles are not considered.

DOI： 10.1016/j.isatra.2020.07.009

Web of Science

自動走行技術を応用した生産現場の省力化への取り組み

PubMed

Modeling Car-Following Behavior in Downtown Area based on Unsupervised Clustering and Variable Selection Method 査読有り

Duc-An Nguyen, Jude Nwadiuto, Hiroyuki Okuda, Tatsuya Suzuki

IEEE International Conference on Systems, Man, and Cybernetics 2020 (SMC2020) 頁： 1 - 6 2020年10月

Drivers' driving style and their take-over-control judgment

Matsumuro Miki, Miwa Kazuhisa, Okuda Hiroyuki, Suzuki Tatsuya, Makiguchi Minoru

TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR 74 巻頁： 237 - 247 2020年10月

Model predictive driving for tractor-trailer mobile robot with an omni-directional tractor 査読有り

Nobuaki Ito, Hiroyuki Okuda, Tatsuya Suzuki,

2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) 頁： 1530 - 1533 2020年9月

Scenario-Based Model Predictive Speed Controller Considering Probabilistic Constraint for Driving Scene with Pedestrian 査読有り

Arun Muraleedharan, Anh Tuan Tran, Hiroyuki Okuda, Tatsuya Suzuki

IEEE Intelligent Transportation Systems Society Conference(ITSC 2020) 頁： 1350 - 1356 2020年9月

Multi-Task Model Predictive Control Based on Continuation with Intermediate Mode 査読有り

Kohei Honda, Hiroyuki Okuda, Tatsuya Suzuki

IEEE Intelligent Transportation Systems Society Conference(ITSC 2020) 頁： 713 - 719 2020年9月

Model Structure Identification of Hybrid Dynamical Systems based on Unsupervised Clustering and Variable Selection 査読有り

Duc An Nguyen, Jude Nwadiuto, Hiroyuki Okuda, Tatsuya Suzuki

21st IFAC World Congress 頁： 1 - 6 2020年7月

Scenario-based stochastic MPC for vehicle speed control considering the interaction with pedestrians 査読有り

Anh Tuan Tran, Arun Muraleedharan, Hiroyuki Okuda, Tatsuya Suzuki

21st IFAC World Congress 頁： 1 - 7 2020年7月

Decision-Making in Interactions Between Two Vehicles at a Highway Junction 査読有り

Asaya Shimojo, Yuki Ninomiya, Shota Matsubayashi, Kazuhisa Miwa, Hitoshi Terai, Hiroyuki Okuda, Tatsuya Suzuki

HCI in Mobility, Transport, and Automotive Systems. Driving Behavior, Urban and Smart Mobility 12213 巻頁： 104 - 113 2020年7月

Piecewise ARXモデルに基づくインタラクティブな運転行動のモデリングと解析査読有り

山口拓真, 奥田裕之, 鈴木達也

知能と情報(日本知能情報ファジィ学会誌) 32 巻 ( 3 ) 頁： 713 - 721 2020年6月

Model Structure Identification of Hybrid Dynamical Systems based on Unsupervised Clustering and Variable Selection

Duc-An Nguyen, Nwadiuto Jude, Okuda Hiroyuki, Suzuki Tatsuya

IFAC PAPERSONLINE 53 巻 ( 2 ) 頁： 1090 - 1095 2020年

Scenario-based stochastic MPC for vehicle speed control considering the interaction with pedestrians

Anh-Tuan Tran, Muraleedharan Arun, Okuda Hiroyuki, Suzuki Tatsuya

IFAC PAPERSONLINE 53 巻 ( 2 ) 頁： 15325 - 15331 2020年

Scenario-based model predictive speed controller considering probabilistic constraint for driving scene with pedestrian

Muraleedharan Arun, Anh-Tuan Tran, Okuda Hiroyuki, Suzuki Tatsuya

2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) 2020年

Multi-task Model Predictive Control based on Continuation with Intermediate Mode

Honda Kohei, Okuda Hiroyuki, Suzuki Tatsuya

2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) 2020年

Modeling Car-Following Behavior in Downtown Area based on Unsupervised Clustering and Variable Selection Method

Duc-An Nguyen, Nwadiuto Jude, Okuda Hiroyuki, Suzuki Tatsuya

2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) 頁： 3714 - 3720 2020年

Model predictive driving for tractor-trailer mobile robot with an omni-directional tractor

Ito Nobuaki, Okuda Hiroyuki, Suzuki Tatsuya

2020 59TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) 頁： 1530 - 1533 2020年

全方位移動口ボットによるモデル予測型自動牽引

伊藤暢章, 奥田裕之, 鈴木達也

ロボティクス・メカトロニクス講演会講演概要集 2020 巻 ( 0 ) 頁： 1A1-G06 2020年

奥田裕之, 鈴木達也

東海支部総会講演会講演論文集 2020.69 巻 ( 0 ) 頁： 420 2020年

Analysis and Modeling of Real World Car-Following Driving in Downtown Area Based on Variable Structured Piecewise Linear Model 査読有り

Jude C. Nwadiuto, Hyuntai Chin, Hiroyuki Okuda, Tatsuya Suzuki

Proc. of 2019 IEEE Intelligent Transportation Systems Conference(ITSC) 頁： 1211-1216 2019年10月

Analysis and Modeling of Real World Car-Following Driving in Downtown Area Based on Variable Structured Piecewise Linear Model 査読有り国際誌

Jude C. Nwadiuto, Hyuntai Chin, Hiroyuki Okuda, Tatsuya Suzuki

Proc. of 2019 IEEE Intelligent Transportation Systems Conference(ITSC) 頁： 1211-1216 2019年10月

A model predictive control-based lane merging strategy for autonomous vehicles 査読有り

Anh-Tuan Tran, Kawaguchi Masato, Okuda Hiroyuki, Suzuki Tatsuya

2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) 頁： 594-599 2019年

Improvement of Control Performance of Sampling Based Model Predictive Control using GPU

Muraleedharan Arun, Okuda Hiroyuki, Suzuki Tatsuya

2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) 頁： 1999-2004 2019年

Analysis and Modeling of Real World Car-Following Driving in Downtown Area Based on Variable Structured Piecewise Linear Model 査読有り

Nwadiuto Jude, Chin Hyuntai, Okuda Hiroyuki, Suzuki Tatsuya

2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) 頁： 1211 - 1216 2019年

A model predictive control-based lane merging strategy for autonomous vehicles

Anh-Tuan Tran, Kawaguchi Masato, Okuda Hiroyuki, Suzuki Tatsuya

2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) 頁： 594-599 2019年

Improvement of Control Performance of Sampling Based Model Predictive Control using GPU 査読有り国際誌

Muraleedharan Arun, Okuda Hiroyuki, Suzuki Tatsuya

2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) 頁： 1999-2004 2019年

Path tracking control using model predictive control with on GPU implementation for autonomous driving 査読有り国際誌

MURALEEDHARAN Arun, OKUDA Hiroyuki, SUZUKI Tatsuya

Journal of Arid Land Studies 28 巻 ( S ) 頁： 163 - 167 2018年12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：The Japanese Association for Arid Land Studies

Autonomous driving is one of the technologies that will become the next milestone in mobility advancement. Before reaching public roads, autonomous driving is expected in areas where driving is considered to be hard and unsafe, like arid and desert terrains. We are trying to achieve fully automated driverless vehicle in transportation sector. Demand of high performance computation using limited space and minimum energy is a pressing need for the mass application of autonomous driving systems. This paper presents a control system for autonomous driving based on model predictive control (MPC) in which a graphics processing unit (GPU) was used to enhance the computation speed for a better real-time performance. The motion controls for the lateral direction is formulated as a model predictive control problem. The lateral MPC was partly executed using GPU for speed enhancement. The implemented MPC package and its adaptation to GPU will make autonomous driving feasible on relatively smaller and cheaper on-board computers, it will also be beneficial to the developers and researchers in various fields other than control engineering field.

DOI： 10.14976/jals.28.s_163

Energy evaluation of CACC and ACC system in auto mixed platoon 査読有り国際誌

SINHA Srishti, CHIN Hyuntai, OKUDA Hiroyuki, SUZUKI Tatsuya

Journal of Arid Land Studies 28 巻 ( S ) 頁： 159 - 162 2018年12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：The Japanese Association for Arid Land Studies

In the last decade, severity of climatic condition in arid and desert areas, has increased phenomenally. Disaster control mechanism in arid and desert area needs to be supplemented by fleet of driverless vehicles, which could be remotely controlled. It would be prudent not to create permanent infrastructure for disaster control services in order to reduce cost component and quick services. This paper presents simulator result for the Cooperative Adaptive Cruise Control (CACC) system based on a Model Predictive Control. CACC is one of promising technologies to achieve the group-level safety and energy aware control in driverless vehicle platoon. The system allows information exchange through Vehicle to Vehicle (V2V) or Individual to Vehicle (I2V) communication. Since the proposed method enables the application of CACC to mixed vehicle fleet consists of manual-driven vehicles and driver-less vehicles. This technology also realizes driver-less accompany vehicle to improve vehicle level redundancy in severe driving environment. Proposed system is implemented and its performance is tested in a simulator experiment.

DOI： 10.14976/jals.28.s_159

屋外の路面状況を考慮した電動車いすの人間機械協調制御

Simultaneous Realization of Planning and Control for Lane-change Behavior Using Nonlinear Model Predictive Control 査読有り

Nobuto Sugie, Hiroyuki Okuda, Tatsuya Suzuki, Kentaro Haraguchi, Zibo Kang

2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC) 頁： 2406-2411 2018年11月

Sampling Based Predictive Control with Frequency Domain Input Sampling for Smooth Collision Avoidance 査読有り

Hiroyuki Okuda, Yingqi Liang, Tatsuya Suzuki

2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC) 頁： 2426-2431 2018年11月

本線走行車の合流受容性を考慮したモデル予測型合流車速制御手法の提案査読有り

原田晃汰, 奥田裕之, 鈴木達也, 西郷慎太朗, 井上聡

自動車技術会論文集 49 巻 ( 5 ) 頁： 1011-1017 2018年9月

INSとマルチレイヤーLiDARを用いた自動運転車両のための高精度自己位置推定査読有り

赤井直紀, 竹内栄二朗, 山口拓真, ルイス洋一モラレス, 吉原佑器, 奥田裕之, 鈴木達也, 二宮芳樹

自動車技術会論文集 49 巻 ( 3 ) 頁： 675-681 2018年5月

Simultaneous Realization of Planning and Control for Lane-change Behavior Using Nonlinear Model Predictive Control

Sugie Nobuto, Okuda Hiroyuki, Suzuki Tatsuya, Haraguchi Kentaro, Kang Zibo

2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) 頁： 2406-2411 2018年

Sampling Based Predictive Control with Frequency Domain Input Sampling for Smooth Collision Avoidance

Okuda Hiroyuki, Liang Yingqi, Suzuki Tatsuya

2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) 頁： 2426-2431 2018年

杉山俊一郎, 奥田裕之, 稲垣伸吉, 鈴木達也

東海支部総会講演会講演論文集 2018.67 巻 ( 0 ) 頁： 404 2018年

Parking Trajectory Planning Using Multiresolution State Roadmaps 査読有り

Yuichi Tazaki, Hiroyuki Okuda, Tatsuya Suzuki

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

Real-Time Collision Avoidance Control Based on Continuation Method for Nonlinear Model Predictive Control with Safety Constraint 査読有り

Hiroyuki Okuda,Nobuto Sugie, Tatsuya Suzuki

Proceedings of The 2017 Asian Control Conference 頁： 1086-1091 2017年12月

Parking Trajectory Planning Using Multiresolution State Roadmaps

Tazaki Yuichi, Okuda Hiroyuki, Suzuki Tatsuya

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2 巻 ( 4 ) 頁： 298 - 307 2017年12月

Autonomous Driving Based on Accurate Localization Using Multilayer LiDAR and Dead Reckoning 査読有り

Naoki Akai, Luis Yoichi Morales, Takuma Yamaguchi, Eijiro Takeuchi, Yuki Yoshihara, Hiroyuki Okuda, Tatsuya Suzuki, and Yoshiki Ninomiya

Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems 頁： 1147-1152 2017年10月

Identification of Time-Varying Parameters of Hybrid Dynamical System Models and Its Application to Driving Behavior 査読有り

Thomas WILHELEM, Hiroyuki OKUDA, Tatsuya SUZUKI

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 頁： 2095-2105 2017年10月

Identification of Time-Varying Parameters of Hybrid Dynamical System Models and Its Application to Driving Behavior

Wilhelem Thomas, Okuda Hiroyuki, Suzuki Tatsuya

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A 巻 ( 10 ) 頁： 2095-2105 2017年10月

Design of Automated Merging Control by Minimizing Decision Entropy of Drivers on Main Lane 査読有り

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

Proceedings of 2017 IEEE Intelligent Vehicles Symposium 頁： 640 -646 2017年7月

Design of Automated Merging Control by Minimizing Decision Entropy of Drivers on Main Lane 査読有り国際誌

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

Proceedings of 2017 IEEE Intelligent Vehicles Symposium 頁： 640 -646 2017年7月

Design of Automated Merging Control by Minimizing Decision Entropy of Drivers on Main Lane 査読有り

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

Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV) 2017年6月

Energy Consumption Evaluation Based on a Personalized Driver-Vehicle Model 査読有り

Wilhelem Thomas, Okuda Hiroyuki, Levedahl Blaine, Suzuki Tatsuya

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 18 巻 ( 6 ) 頁： 1468 - 1477 2017年6月

運転教習員型運転支援の提案とその評価査読有り

山口拓真, 奥田裕之, 鈴木達也, 早川聡一郎, 池浦良淳, 武藤健二, 伊藤隆文

自動車技術会論文集 48 巻 ( 3 ) 頁： 717-724 2017年5月

Autonomous Driving Based on Accurate Localization Using Multilayer LiDAR and Dead Reckoning

Akai Naoki, Morales Luis Yoichi, Yamaguchi Takuma, Takeuchi Eijiro, Yoshihara Yuki, Okuda Hiroyuki, Suzuki Tatsuya, Ninomiya Yoshiki

2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) 頁： . 2017年

Realtime collision avoidance control based on continuation method for nonlinear model predictive control with safety constraint

Okuda Hiroyuki, Sugie Nobuto, Suzuki Tatsuya

2017 11TH ASIAN CONTROL CONFERENCE (ASCC) 頁： 1086 - 1091 2017年

Design of Automated Merging Control by Minimizing Decision Entropy of Drivers on Main Lane

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

2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017) 頁： 640-646 2017年

ROS を用いた SLAMと電動車椅子の操作支援制御

杉山俊一郎, 奥田裕之, 稲垣伸吉, 鈴木達也

ロボティクス・メカトロニクス講演会講演概要集 2017 巻 ( 0 ) 頁： 2P2-D05 - D05 2017年

記述言語：日本語出版者・発行元：一般社団法人日本機械学会

With advance of aging society, the number of users of electric wheelchairs is increasing. However, operating an electric wheelchair in confined spaces requires skill because risk of collisions is high. Then opportunity of going out is lost for unskilled users. This paper presents an obstacle avoiding support system for electric wheelchairs in unknown environment where severe control is required such as a narrow way and an elevator. User's inputs are adjusted depending on the relative position of the robot and obstacles which is estimated by SLAM (Simultaneous Localization and Mapping) using LRF (Laser Range Finder) and rotary encoders. The availability is verified in some experiments.

DOI： 10.1299/jsmermd.2017.2p2-d05