| Project/Area Number |
21K17781
|
|---|
| Research Category |
Grant-in-Aid for Early-Career Scientists
|
| Allocation Type | Multi-year Fund |
|---|
| Review Section |
Basic Section 61020:Human interface and interaction-related
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
Wang Zheng 東京大学, 生産技術研究所, 特任助教 (20837497)
|
|---|
| Project Period (FY) |
2021-04-01 – 2024-03-31
|
| Project Status |
Completed (Fiscal Year 2023)
|
| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2022: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2021: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
|
|---|
| Keywords | HMI / Automated driving / Machine learning / Intelligent vehicles / Driver behavior modeling / Human-centered design / Shared control / Haptic interface / Mutual adaptation |
|---|
| Outline of Research at the Start |
This research will focus on driver-automation mutual adaptation with haptic interface. First, I will test the hypothesis that the driver-automation mutual adaptation could be modeled using data-driven approach based on driver’s integration of visual and haptic sensory information. Second, it is expected that drivers would feel the mutually adaptive system more trustworthy and would be more satisfied compared to one-way adaptation. Third, driver-automation cooperative performance, including reduced driver workload and lane departure risks, will be improved through mutual adaptation.
|
| Outline of Final Research Achievements |
This research focuses on driver-automation mutual adaptation and the development of haptic shared control systems for enhanced automated driving experiences. At the beginning of the project, a robust lateral control model for human drivers was established, demonstrating superior accuracy in identifying driver behavior. After that, A steering assistance system involving a shared control strategy was developed for driver override in automated vehicles. The system considers the potential driver demand for override when the vehicle initiates a fail-safe maneuver. A shared control strategy based on driver controllability is adopted to smoothly transfer driving authority when the vehicle is out of danger. Furthermore, novel driving simulator studies were conducted to test mutual adaptive shared control systems with updated trust values, showcasing improvements in lane-keeping performance and user satisfaction.
|
| Academic Significance and Societal Importance of the Research Achievements |
My research provides insights on understanding how driver interacts with haptic shared control system. Moreover, by designing a shared control system, my research helps to raise people’s motivation and ability to move that would expand their life space by improving driving safety and comfort.
|
