Research | 東京大学　中野研究室

Overview

Mobility Measurement and Controlling

Based on the fundamental fields of mechanics, vibration, and control engineering, researches on state monitoring, ergonomics, human-machine interface, automated driving, and cooperative systems related to mobility are conducted. Non-technical issues, called ELSI, are also being addressed, with the aim of implementing these technologies in society.

Major Research Themes (click for details)

Driver Initiated Take-over during driver assistance with signal recognition: When advanced driver assistance systems (ADAS) with signal recognition are used, the driver is requested to take over driving to avoid an accident when the system fails to detect a signal or detects a false signal. The purpose of this study is to evaluate the possibility of safe driver-initiated intervention in the event of undetected or false detection by the driver assistance system, and to propose and evaluate a human-machine interface that promotes appropriate intervention, through driving simulator experiments.
Evaluation of Performance of Shared Control: Shared control is a system controlling something cooperating with a human. A part of advanced driver assist systems of automobiles corresponds to it. Our laboratory is conducting research on a haptic steering guidance system as an example of the shared control.
Haptic Steering Assistance Based on Prediction of the Future Trajectory in Line with the Intention of the Driver: This research explores the development and evaluation of machine learning models and a haptic steering assistance system which can predict future trajectory to assist human driver. Several driving experiments to evaluate the proposed system are conducted.
Fallback System of Automated Driving Vehicle Incorporating Potential Driver Intervention: This is a fallback system that can guide a vehicle to a minimal risk condition while responding to potential human intervention in the event of system malfunction or exit from Operational Design Domain (ODD). When the automated vehicle encounters system malfunction or ODD exit, the fallback system can take over the vehicle, and then check if the driver is ready to take over. If the driver is ready, the system will assist the driver with shared control, while ensuring path to a minimal risk condition. Otherwise, the system will reject the driver and reach a safe stop.
Predicting Readiness and Performance of a Driver for Transitions from Automated to Manual Driving: In level 3 automated driving, a fallback ready user is necessary, that should be responsive to system’s request to intervene and respond appropriately. This is a challenging task in that it is difficult to decide the sufficient time for a driver to safely take over the vehicle. The main goal of this research is to model drivers' takeover behaviors by integrating a variety of factors (including system-related, scenario-related, and human-related factors), so that drivers' takeover behaviors could be predicted in advance and systems could adapt their strategies accordingly to ensure safe takeovers.
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.
Energy Harvesting in Rotating Body: This is an energy harvester in rotational motion which can convert the vibration and rotation energy into electricity based on the piezoelectric effect. Thus, the promising application is to power the wireless sensors installed in the rotational environment, such as the tire pressure monitoring system (TPMS). To enhance energy harvesting performance, a multi-stable nonlinear energy harvester is proposed in rotational motion.
Estimation of Contact Condition between Wheel and Rail Using Instrumented Wheelset: Generally, running safety of railroad vehicles is evaluated by running tests with an instrumented wheelset called a PQ-wheel axle, which can measure vertical load P, lateral load Q, and front-rear tangential force T acting between the wheel and rail by a new continuous method. Aiming to improve the accuracy of running safety evaluation when passing through curves, a method to determine the contact condition between wheels and rails, which is difficult to measure, using the measured values of the instrumented wheelset is examined.
Unified Traffic Control System for Railway and Road Vehicles Using Mobile Phone Line: 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: Pilot test to run an automated driving bus (Level 2 operation) between Kashiwanoha Campus Station and Kashiwa campus of the University of Tokyo every day (weekdays only) started in November 2019 implemented by Kashiwa ITS Promotion Council. To link these activities to the social implementation of Level 4 automated driving services in the Kashiwanoha area of Kashiwa City, a six-party consortium led by the University of Tokyo was entrusted with “RoAD to the L4 project (Theme 4)”, called as CooL4, of the Ministry of Economy, Trade and Industry and the Ministry of Land, Infrastructure and Transport.
Building the Method for Social Implementation of Automated Driving Technology Complying with Actual State Based on ELSI: ELSI stands for Ethical, Legal and Social Implications/Issues. There have been efforts in various fields to study and deal with ELSIs that arise with the development of new science and technology. This study examined how automated driving technology should be implemented in society, based on the fundamental question, "Can humans and society accept the mistakes made by machines?”. This project was supported by the Research and Development Center for Social Technology (RISTEX) of the Japan Science and Technology Agency (JST) under the "Research and Development Program for Comprehensive Application of Science and Technology to Ethical, Legal, and Social Issues (ELSI)" (RInCA) (FY 2020), and was conducted by the University of Tokyo, Meiji University, and the University of Tsukuba. The project was conducted by the University of Tokyo, Meiji University, and University of Tsukuba.

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