Driver-automation mutual adaptation: modeling, design, and evaluation of haptic interface for cooperative driving tasks

2023 Fiscal Year Final Research Report

• 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
• Keywords: HMI / Automated driving / Machine learning / Intelligent vehicles / Driver behavior modeling

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.

Free Research Field

Human-Machine Interaction

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.