Mobility Measurement and Controlling

We are conducting research on measurement and control in mobility based on knowledge of mechanical engineering, including active vibration control, energy harvesting (vibration power generation), condition monitoring, biological signal measurement, haptic guidance steering, in-vehicle traffic signal, integrated traffic control of automobile and railroad, evaluation on human machine interface for automobile.

Major Research Themes (click for details)
Research and Development of Human Machine Interface for Driver Initiated Take-over
To improve driver‐initiated take‐over while using level 2 (L2) automated driving in general roads, this study firstly analyzed the differences in driver behaviors during L2 automated driving and manual driving. Afterwards, an HMI was proposed to improve drivers’ understandings on L2 systems, by comprehensively presenting the real time results of image recognition by the systems to drivers, and its effectiveness was evaluated with driving simulator experiments.

Shared Control
Shared control refers to a system in which a machine and a human perform control together, as a part of the driving assistance technology for vehicles. We conduct research on shared control, where force-sensitive steering that supports steering by applying a haptic force to the steering wheel according to the deviation from the ideal trajectory. By modeling of human musculoskeletal system, the effectiveness of shared control are investigated through driving simulator experiments.
Driver Model for Shared Control
Understanding of driver behavior based on measurements and modeling is crucial to design and evaluation of driver‐automation shared control system. Our aim is to propose a driver model with integration of visual guidance from road ahead and haptic guidance from a steering system. It is hypothesized that a driver relies on visual and haptic guidance through a weighting process.
Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System
This study explored a new haptic steering interaction method, including the design and evaluation of an Intention Based Haptic Steering ( system Such an intention based method can support both lane keeping and lane changing assistance, by detecting a driver’s Lane Change ( intention A driving simulator experiment demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.
Trajectory Prediction of Surrounding Vehicles based on Traffic Scenario Understanding
Accurate and fast trajectory prediction of surrounding road users is critical to improve the intelligence of autonomous driving systems In complex traffic scenario, road users with different kinds of behaviors and styles and road with different kinds of areas and markers brings complexity to the environment, which requires considering interactions among road users and road structure and traffic rules, when anticipating their future trajectories This study proposes a long term parallel interactive trajectory prediction method based on scenario understanding.
Dynamic Driving Task Fallback System for an Automated Vehicle Encountering Sensor Failure in Monitoring Driving Environment
This is an embedded fail-safe system, functioning when an automated vehicle encounters such severe sensor failure that it cannot continue original driving task. Fallback system is supposed to take over the failed vehicle when sensor failure occurs. This research applies it in the fallback process on highway, during which the failed vehicle is instructed to safely leave the lane for a stop. Validity is shown through numerical simulations.

Movie of Fallback control process 1
Movie of Fallback control process 2
Energy Harvesting in Rotating Body
Generating energy in a rotating tire, where sensors are demanded, is proposed. It is tried to harvest more energy by enhancing the vibration with stochastic resonance caused by periodic force and vibration due to roughness of road.
Decreased Deceleration Detection of Railway Vehicle in Snow Condition
When slip between a rail and a wheel occurs, brake distance is extended, which demands particular caution by a driver. As the site and time of the occurrence is limited, it is effective to share the information among the drivers to avoid unsafe operation. The system to detect the slip occurrence from the acceleration of the vehicle and share it with other drivers is proposed and its validity is examined.
Estimation of Condition Between Rail and Wheel from Measured Values of a PQ Wheel
The derailment quotient, defined as 𝑄⁄𝑃 where 𝑃 is wheel load and 𝑄 is lateral force, is a common index for safety in railway vehicles. The PQ wheel is widely used to measure 𝑃 and 𝑄. However, the value of this index is decreased by the wetness of the rail, which leads underestimation of the value. Hence it is important to know friction coefficient between rail and wheel. Currently it is done by a human on site, who knows the weather there. To save labor, we propose an objective method to judge the wetness of the rail based on the data measured by the PQ wheel.
Unified Traffic Control System for Railway and Road Vehicles
Unified traffic control system for railway and road vehicles using mobile phone line is proposed. The demonstration is carried out at ITS experimental field in Kashiwa campus, including the railway test track, the test road, and the railway crossings.
Activities to Realize Level 4 Cooperated Automated Mobility Service
The Kashiwa ITS Promotion Council is the main implementer of this project, and automated buses (Level 2 operation) are running between Kashiwanoha Campus Station and the University of Tokyo's Kashiwa Campus every day (weekdays only). In order to link these activities to the social implementation of Level 4 automated driving services, the use of a cooperative system is being considered and developed.
Building the Method for Social Implementation of Automated Driving
Technology Complying with Actual State Based on ELSI
ELSI is an abbreviation for “Ethical, Legal and Social Implications/Issues”. In this project, we will identify ELSI related to automated driving technology and examine how this technology should be implemented in society. As part of our activities, we are inviting the general public to ride the automated driving bus (between Kashiwa‐no‐ha Campus Station and the University of Tokyo Kashiwa Campus) that is being operated by Kashiwa ITS Promotion Council, and extracting their opinions. This research and development project has been adopted by the R&D Program Responsible Innovation with Conscience and Agility (FY2020‐) by Research Institute of Science and Technology (RISTEX), Japan Science and Technology Agency (JST).
Modeling of a Truck Driver During Lane Change Using Fuzzy Logic
Lane changing is one of the most frequent and fundamental activities in highway driving However, dangerous lane changes are also one of the major causes of traffic accidents and violations Lack of confirmation due to driver inattention and driving fatigue is considered the primary cause. This study aims to propose a driver's lane change decision making model that incorporates factors, including inter vehicle distance and relative velocity, into fuzzy inference Membership functions of the fuzzy model will be determined based on results from manual driving simulator experiments To accommodate various driving habits and preferences, the proposed fuzzy model is designed with three types aggressive (Model A), moderate (Model M), and conservative (Model C) Additionally, each driver's manual driving data is used to generate personalized models (Model P) The validity of the models is examined through mathematical simulations.
Steering Controller Design of Automated Driving Bus
There are high expectations for the development of automated buses and driverless bus operation in developed countries because of the aging population and the gradual shortage of transportation. Since only onboard sensors are not enough for measuring precise position of the vehicle, infrastructures such as magnetic markers are expected to correct it. However, comparison between infrastructures and sensors, and the appropriate interval of markers have not been stated explicitly.
Experiment Device