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This study investigates dynamic control allocation for Advanced Driver Assistance Systems (ADAS) in Intelligent Transportation Systems through co-simulation (Prescan, CarSim, Simulink) and driver-in-the-loop experiments. Analysis of emergency braking scenarios reveals ADAS achieves the fastest response (1.8–2.0 s), compared to trust-oriented drivers (90th percentile: 5.011–5.377 s) and alert drivers (4.043–4.518 s). Based on these findings, we propose a dynamic allocation strategy: drivers maintain control when available response time exceeds their reaction threshold, otherwise ADAS automatically intervenes. This approach enhances safety and reliability in complex driving environments, advancing human-machine collaborative driving through both theoretical frameworks and practical applications.

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Research on Dynamic Allocation of Driving Control Authority Based on Driver Trust and Safety Threshold

  • 会议论文

  • 发表日期：

    2026年

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  • Quan Yu,
  • Jingfeng Zou,
  • Xiaodong Wu

摘要

This study investigates dynamic control allocation for Advanced Driver Assistance Systems (ADAS) in Intelligent Transportation Systems through co-simulation (Prescan, CarSim, Simulink) and driver-in-the-loop experiments. Analysis of emergency braking scenarios reveals ADAS achieves the fastest response (1.8–2.0 s), compared to trust-oriented drivers (90th percentile: 5.011–5.377 s) and alert drivers (4.043–4.518 s). Based on these findings, we propose a dynamic allocation strategy: drivers maintain control when available response time exceeds their reaction threshold, otherwise ADAS automatically intervenes. This approach enhances safety and reliability in complex driving environments, advancing human-machine collaborative driving through both theoretical frameworks and practical applications.