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2020 Fiscal Year Research-status Report

Driver behavior modeling and its application to a guidance-as-needed steering system for individualized lane change assistance

Research Project

Project/Area Number 19K20318
Research InstitutionThe University of Tokyo

Principal Investigator

王 正  東京大学, 生産技術研究所, 特任助教 (20837497)

Project Period (FY) 2019-04-01 – 2022-03-31
KeywordsHaptic interface / Driver behavior modeling / Lane change assistance / Shared control / Guidance-as-needed
Outline of Annual Research Achievements

Our research focuses on modeling lane change behavior of individual driver and designing a guidance-as-needed steering system through haptic interface.
In FY 2020, our research achievements are summarized as follows:
1. A personalized driver decision model for discretionary lane-change initiation based on reinforcement learning was developed through a driving simulator experiment with a number of subjects.
2. A surface Electromyography (sEMG) controlled steering system was proposed for pedestrian collision avoidance through a rapid lane change task, and its performance was evaluated by comparing with a haptic guidance steering system.
3. An adaptive haptic guidance steering system via forearm sEMG measurement was designed, and a driving simulator experiment with a number of subjects was conducted to evaluate its effect on driving performance. The results indicate that the adaptive authority of haptic guidance (haptic guidance decreases when driver grip strength increases) yielded lower driver workload and reduced the lane departure risk compared to manual driving and fixed authority haptic guidance.

Current Status of Research Progress
Current Status of Research Progress

3: Progress in research has been slightly delayed.

Reason

1. Based on the driver model developed in the last fiscal year, a new driving simulator experiment with a number of participants was conducted to collect the discretionary lane-change decision data, which are used to train a novel reinforcement learning model, and thereby a personalized lane change model was developed.
2. By real-time measuring driver eye movement or forearm muscle activity, adaptive haptic guidance systems were designed to assist individual drivers for lane change tasks. The results from driving simulator experiments with a number of participants demonstrate that the designed systems are adaptive and capable to improve driving safety and comfort.
3. The real-vehicle experiment to evaluate the proposed adaptive haptic guidance system was designed, while it was not smoothly carried out as planned due to COVID-19.

Strategy for Future Research Activity

In the next fiscal year, our research activities will focus on the following aspects:
1. Real-vehicle experiments will be conducted to evaluate the effectiveness of adaptive haptic guidance steering system on lane change performance by comparing with fixed authority haptic guidance and manual driving. Moreover, the effect of adaptive haptic guidance on distracted driver behavior will be investigated.
2. The driver behavior measured in real-vehicle experiments will be analyzed for better understanding of driver's integration of visual and haptic information during a lane change task.

Causes of Carryover

In this fiscal year, we have focused on system design and evaluation by driving simulator experiments. The real-vehicle experiments were postponed due to COVID-19. In the next fiscal year, we will conduct real-vehicle experiments by recruiting subjects. In addition, we will publish several journal papers to report our results, and therefore, budget for publication fee will be needed.

  • Research Products

    (6 results)

All 2021 2020

All Journal Article (4 results) (of which Peer Reviewed: 4 results) Presentation (2 results) (of which Int'l Joint Research: 2 results)

  • [Journal Article] Adaptive Driver-Automation Shared Steering Control via Forearm Surface Electromyography Measurement2021

    • Author(s)
      Wang Zheng、Suga Satoshi、Nacpil Edric John Cruz、Yan Zhanhong、Nakano Kimihiko
    • Journal Title

      IEEE Sensors Journal

      Volume: 21 Pages: 5444~5453

    • DOI

      10.1109/JSEN.2020.3035169

    • Peer Reviewed
  • [Journal Article] Surface Electromyography-controlled Pedestrian Collision Avoidance: A Driving Simulator Study2021

    • Author(s)
      Nacpil Edric John Cruz、Wang Zheng、Yan Zhanhong、Kaizuka Tsutomu、Nakano Kimihiko
    • Journal Title

      IEEE Sensors Journal

      Volume: - Pages: 1~1

    • DOI

      10.1109/JSEN.2021.3070597

    • Peer Reviewed
  • [Journal Article] Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System2020

    • Author(s)
      Yan Zhanhong、Yang Kaiming、Wang Zheng、Yang Bo、Kaizuka Tsutomu、Nakano Kimihiko
    • Journal Title

      IEEE Transactions on Intelligent Vehicles

      Volume: - Pages: 1~1

    • DOI

      10.1109/TIV.2020.3044180

    • Peer Reviewed
  • [Journal Article] Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning2020

    • Author(s)
      Liu Zhuoxi、Wang Zheng、Yang Bo、Nakano Kimihiko
    • Journal Title

      IEEE International Conference on Systems, Man and Cybernetics

      Volume: - Pages: 457~463

    • DOI

      10.1109/SMC42975.2020.9283222

    • Peer Reviewed
  • [Presentation] Surface Electromyography-controlled Automotive Braking Assistance System Using Deep Learning Method2021

    • Author(s)
      Gia Quoc Bao Tran
    • Organizer
      12th International Conference on Applied Human Factors and Ergonomics (AHFE 2021)
    • Int'l Joint Research
  • [Presentation] Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning2020

    • Author(s)
      Liu Zhuoxi
    • Organizer
      2020 IEEE International Conference on Systems, Man and Cybernetics (SMC 2020)
    • Int'l Joint Research

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Published: 2021-12-27  

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