2024/07/14 更新

写真a

ハヤシ ヒロアキ
林 弘昭
所属
理工学術院 創造理工学部
職名
助教

学歴

  • 2020年04月
    -
    2023年03月

    早稲田大学   大学院創造理工学研究科   総合機械工学専攻  

  • 2018年04月
    -
    2020年03月

    早稲田大学   大学院創造理工学研究科   総合機械工学専攻  

  • 2014年04月
    -
    2018年03月

    早稲田大学   創造理工学部   総合機械工学科  

研究キーワード

  • ドライバーモニタリング

  • 行動分析

  • 機械学習

  • 画像処理

  • 人共存環境下での自律移動

受賞

  • 若手優秀講演フェロー賞

    2019年05月   日本機械学会   テイクオーバー時の認知的関与度の推定に関する研究~基準視線パターンの導出と視線誘導支援システムの評価~  

メディア報道

  • 奇才の早稲田軍団、登竜門の「発明コン」で決勝進出 自動運転切り替え技術

    インターネットメディア

    2018年06月

 

論文

  • Image Inpainting を用いたハンドル映り込み画像における運転者姿勢推定の高精度化

    岩崎 陽馬, 林 弘昭, 亀﨑 允啓, 菅野 重樹

    自動車技術会論文集   54 ( 3 ) 528 - 533  2023年

    DOI

  • Innovation by Connecting People, Skill, and Value: A Community Platform for Collaborative Job Hunting

    Namiko Saito, Peizhi Zhang, Hiroaki Hayashi, Shigeki Sugano, Kinji Mori

    Proceedings - 2023 IEEE 15th International Symposium on Autonomous Decentralized Systems, ISADS 2023    2023年

     概要を見る

    These days, value structure and social structure are changing with the background of immigration, globalization, and diversification. In many countries, such as Japan, a foreign workforce is introduced due to ageing citizens and labour shortages. Such diversification would be a great opportunity to create innovation. Innovation is often achieved when ideas from different perspectives synergize with each other. In this paper, we focus on the job hunting scene and suggest recruiting diverse and cooperative teams as one form of recruitment. Conventional job-hunting services and Social networking services (SNSs) are limited to matching labour supply and demand or attracting people with similar values. To help build and recruit diverse and cooperative teams, we propose a system to build a platform to connect people with different backgrounds, skills, and values and promote their cooperation. With the system, job hunters could find a team where members can leverage each other's strengths and compensate for each other's weaknesses. Furthermore, companies could effectively evaluate the diversity and cooperativeness of teams for hiring decisions. The evaluation scenario demonstrated that the proposed system could increase both job hunter and recruiter satisfaction levels and social impact.

    DOI

    Scopus

  • JointFlow: A Foot Motion Tracking Model Combining Pose Estimation Model with Optical Flow

    Hiroaki Hayashi, Hirofumi Aoki, Mitsuhiro Kamezaki, Kan Shimazaki, Kunitomo Aoki, Shigeki Sugano

    IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC   2022-October   2875 - 2881  2022年

    担当区分:筆頭著者

     概要を見る

    Understanding driver's foot behavior helps reduce pedal-related traffic accidents. Conventional systems introduced wearable devices to track drivers' foot motion and tracking drivers' feet using non-contact sensors is still challenging. Moreover, to better understand driver's foot behavior, it is required to stably track drivers' small foot motion and at the joint level. To cope with this problem, we develop a novel foot motion tracking system, referred to as 'JointFlow.' This system integrates a pose estimation model, i.e., OpenPose, with Optical Flow. At first, a keypoint region-based convolutional neural network (keypoint R-CNN) is trained to detect the joints of the foot. At the same time, the Lucas-Kanade algorithm of Optical Flow is used to calculate the motion of each foot joint between consecutive frames. We implemented and evaluated the system using a real-world driving dataset of 50 drivers. The evaluation result shows that JointFlow could track both small and large foot motion. By comparing with the conventional pose estimation model, we could confirm that JointFlow tracked small foot motion more stable.

    DOI

    Scopus

  • Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

    Hiroaki Hayashi, Naoki Oka, Mitsuhiro Kamezaki, Shigeki Sugano

    International Journal of Intelligent Transportation Systems Research   19 ( 1 ) 167 - 181  2021年04月

    担当区分:筆頭著者

     概要を見る

    In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

    DOI

    Scopus

    4
    被引用数
    (Scopus)
  • Toward Health–Related Accident Prevention: Symptom Detection and Intervention Based on Driver Monitoring and Verbal Interaction

    Hiroaki Hayashi, Mitsuhiro Kamezaki, Shigeki Sugano

    IEEE Open Journal of Intelligent Transportation Systems   2   240 - 253  2021年

    担当区分:筆頭著者

     概要を見る

    Professional drivers are required to safely transport passengers and/or properties of customers to their destinations, so they must keep being mentally and physically healthy. Health problems will largely affect driving performance and sometimes cause loss of consciousness, which results in injury, death, and heavy compensation. Conventional systems can detect the loss of consciousness or urgently stop the vehicle to prevent accidents, but detection of symptoms of diseases and providing support before the driver loses consciousness is more reasonable. It is challenging to earlier detect symptoms with high confidence. Toward solving these problems, we propose a new method with a multi-sensor based driver monitoring system to detect cues of symptoms quickly and a verbal interaction system to confirm the internal state of the driver based on the monitoring results to reduce false positives. There is almost no data that records abnormal conditions while driving and tests with unhealthy participants are dangerous and ethically unacceptable, so we developed a system with pseudo-symptom data and did outlier detection only with normal driving data. From data collection experiments, we defined the confidence level derived from cue signs. The results of evaluation experiments showed that the proposed system worked well in pseudo headache and drowsiness detection scenarios. We found that signs of drowsiness varied with individual drivers, so the multi-sensor based driver monitoring system was proved to be effective. Moreover, we found that there were individual differences in how the cue signs appeared, so we can propose an online re-training method to make the system adapt to individual drivers.

    DOI

    Scopus

    7
    被引用数
    (Scopus)
  • A Robust Driver's Gaze Zone Classification using a Single Camera for Self-occlusions and Non-aligned Head and Eyes Direction Driving Situations

    Catherine Lollett, Hiroaki Hayashi, Mitsuhiro Kamezaki, Shigeki Sugano

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics   2020-October   4302 - 4308  2020年10月

     概要を見る

    Distracted driving is one of the most common causes of traffic accidents around the world. Recognizing the driver's gaze direction during a maneuver could be an essential step for avoiding the matter mentioned above. Thus, we propose a gaze zone classification system that serves as a base of supporting systems for driver's situation awareness. However, the challenge is to estimate the driver's gaze inside not ideal scenarios, specifically in this work, scenarios where may occur self-occlusions or non-aligned head and eyes direction of the driver. Firstly, towards solving miss classifications during self-occlusions scenarios, we designed a novel protocol where a 3D full facial geometry reconstruction of the driver from a single 2D image is made using the state-of-the-art method PRNet. To solve the miss classification when the driver's head and eyes direction are not aligned, eyes and head information are extracted. After this, based on a mix of different data pre-processing and deep learning methods, we achieved a robust classifier in situations where self-occlusions or non-aligned head and eyes direction of the driver occur. Our results from the experiments explicitly measure and show that the proposed method can make an accurate classification for the two before-mentioned problems. Moreover, we demonstrate that our model generalizes new drivers while being a portable and extensible system, making it easy-adaptable for various automobiles.

    DOI

    Scopus

    6
    被引用数
    (Scopus)
  • Development of an Abnormal Sign Detection System based on Driver Monitoring and Voice Interaction for Preventing Medical-Condition-Caused Car Accidents

    Hiroaki Hayashi, Mitsuhiro Kamezaki, Naoki Oka, Shigeki Sugano

    2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020    2020年09月

    担当区分:筆頭著者

     概要を見る

    The number of medical-condition-caused car accidents (MCCCAs) in transport industry (bus, truck, and taxi) recently increases. MCCCAs including cerebrovascular and cardiovascular disease lead to loss of consciousness, thus result in injury and loss of life, and heavy compensation payment. Toward this problem, conventional systems detect closing of eyes, and fallen down state as to prevent car collisions. However, the support is taken after driver losing consciousness. To prevent MCCCAs, it is important to find out abnormal signs before driver losing consciousness. It is challenging to detect abnormal signs not only early but also with high confidence level (CL). This paper proposes a novel method that multi-modally monitors driver to detect abnormal signs which can be cues for estimating a driving-disable state in future and performs voice interaction based on the result of monitoring to clarify the internal state of the driver. Considering no data of abnormal signs, this study developed the system using normal data and pseudo abnormal data, and method of outlier detection was used for abnormal signs detection. As results of experiment, we found the relationship between cue signs and CL, and the proposed system can detect 'sleepiness' state with accuracy of 80%. Voice interaction system did not increase driver's mental demand.

    DOI

    Scopus

    4
    被引用数
    (Scopus)
  • Human-Centered Intervention Based on Tactical-Level Input in Unscheduled Takeover Scenarios for Highly-Automated Vehicles

    Mitsuhiro Kamezaki, Hiroaki Hayashi, Udara E. Manawadu, Shigeki Sugano

    International Journal of Intelligent Transportation Systems Research   18 ( 3 ) 451 - 460  2020年09月

     概要を見る

    Due to functional limitations in certain situations, the driver receives a request to intervene from automated vehicles operating level 3. Unscheduled intervention of control authority would lead to insufficient situational awareness, then this will make dangerous situations. The purpose of this study is thus to propose tactical-level input (TLI) method with a multimodal driver-vehicle interface (DVI) for the human-centered intervention. The proposed DVI system includes touchscreen, hand-gesture, and haptic interfaces that enable interaction between driver and vehicle, and TLI along with such DVI system can enhance situational awareness. We performed unscheduled takeover experiments using a driving simulator to evaluate the proposed intervention system. The experimental results indicate that TLI can reduce reaction time and driver workload, and moreover, most drivers preferred the use of TLI than manual takeover.

    DOI

    Scopus

    8
    被引用数
    (Scopus)
  • A driver situational awareness estimation system based on standard glance model for unscheduled takeover situations

    Hiroaki Hayashi, Mitsuhiro Kamezaki, Udara E. Manawadu, Takahiro Kawano, Takaaki Ema, Tomoya Tomita, Lollett Catherine, Shigeki Sugano

    IEEE Intelligent Vehicles Symposium, Proceedings   2019-June   798 - 803  2019年06月

    担当区分:筆頭著者

     概要を見る

    Highly-automated vehicles operating in level 3 issue a takeover request (TOR) to transfer the control authority from the autonomated driving (AD) system to a human driver when they encounter system limitations. In such 'unscheduled' situations, the driver is required to immediately re-engage in the driving task both physically and cognitively, and perform suitable action, e.g. lane change. Thus, evaluating driver engagement by the AD system would lead to safe takeover. Physical engagement is easily estimated but there are few studies on evaluating cognitive engagement. In this study, we thus developed a driver situational awareness estimation system based on glance information. We first defined seven standard glance areas and driver glance classification model using a convolutional neural network. We then obtained a large amount of glance data when both safe and dangerous takeover situations (lane change) by using a driving simulator, and we derived the standard glance model including the glance area and time, in order to estimate whether driver gained enough cognitive re-engagement in real-time. To evaluate the effectiveness of the proposed model, we created a situational awareness assist system to visually indicate regions with insufficient glance. As a result, we found that the assist system drastically improved driving performance and reduced the number of accidents during takeover.

    DOI

    Scopus

    13
    被引用数
    (Scopus)
  • Tactical-level input with multimodal feedback for unscheduled takeover situations in human-centered automated vehicles

    Udara E. Manawadu, Hiroaki Hayashi, Takaaki Ema, Takahiro Kawano, Mitsuhiro Kamezaki, Shigeki Sugano

    IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM   2018-July   634 - 639  2018年08月

     概要を見る

    Automated vehicles operating in level 3 may request the human driver to intervene in certain situations due to system limitations. Unscheduled transferring of control to manual driving will create safety issues as consequences of inadequate situational awareness and sudden increase of driver workload. In this study, we propose and evaluate tactical-level input (TLI) method with a multimodal human-machine interface (HMI) for driver intervention in short-term system limitations. The HMI system consists of touchscreen, gesture, and haptic interfaces enabling bilateral driver-vehicle interaction. TLI along with the HMI capable of multimodal feedback can provide situation-adaptive spatial information which enhance the driver situational awareness in a short time. To evaluate the proposed system we conducted driving experiments involving unscheduled takeover situations in urban environment using a driving simulator. We analyzed driver reaction times, physiological responses including heart rate, skin conductance and subjective workload as well as qualitative feedback comparing with manual takeover. The results show that TLI can reduce driver workload, reaction times, and improve driver behavior. Moreover, 90% of drivers preferred to use TLI method over manual takeover.

    DOI

    Scopus

    13
    被引用数
    (Scopus)

▼全件表示

共同研究・競争的資金等の研究課題

  • 健康起因事故の未然防止を主目的とした「Virtual Co-Driver」の事業化検証

    科学技術振興機構(JST)  START 社会的還元加速プログラム(SCORE)

    研究期間:

    2019年09月
    -
    2020年03月
     

Misc

産業財産権

  • 状況認識推定システム及び運転支援システム

    亀﨑允啓, 林 弘昭, 岡 直樹, マナワドゥ ウダーラ, 菅野 重樹

    特許権

    J-GLOBAL

 

担当経験のある科目(授業)

  • 理工学基礎実験2A

    早稲田大学  

    2023年04月
    -
    継続中