根据驾驶员实时表现动态调整接管控制权,提升自动驾驶接管安全性与舒适性。
An Adaptive Transition Framework for Game-Theoretic Based Takeover
- 将共享控制建模为合作微分博弈,通过时变目标函数调节控制权。
- 实验显示自适应策略使轨迹偏差减少23%,驾驶者控制努力降低18%。
- 适合关注自动驾驶人机协同、接管安全性的研究人员与工程师。
自动驾驶系统中从自主控制向人类驾驶员的接管至关重要,尤其在驾驶员处于“脱环”(OOTL)状态时,其准备度下降且反应时间延长。现有接管策略多基于固定时间切换,无法反映驾驶员实时表现差异。本文提出一种自适应接管框架,根据驾驶员轨迹的时间与跟踪能力动态调整控制权限。共享控制被建模为合作微分博弈,通过时变目标函数调节控制权,而非直接混合控制扭矩。引入驾驶员特定的状态跟踪矩阵,使接管过程契合个体控制偏好。采用累积轨迹误差指标评估多种策略。基于ISO标准车道变换的人在回路实验表明,自适应策略相比传统方法显著降低轨迹偏离和驾驶者控制努力。实验还证实,根据实时偏差持续调整控制权可增强车辆稳定性并减少接管期间的驾驶负担。
原文摘要 · Abstract (English)
The transition of control from autonomous systems to human drivers is critical in automated driving systems, particularly due to the out-of-the-loop (OOTL) circumstances that reduce driver readiness and increase reaction times. Existing takeover strategies are based on fixed time-based transitions, which fail to account for real-time driver performance variations. This paper proposes an adaptive transition strategy that dynamically adjusts the control authority based on both the time and tracking ability of the driver trajectory. Shared control is modeled as a cooperative differential game, where control authority is modulated through time-varying objective functions instead of blending control torques directly. To ensure a more natural takeover, a driver-specific state-tracking matrix is introduced, allowing the transition to align with individual control preferences. Multiple transition strategies are evaluated using a cumulative trajectory error metric. Human-in-the-loop control scenarios of the standardized ISO lane change maneuvers demonstrate that adaptive transitions reduce trajectory deviations and driver control effort compared to conventional strategies. Experiments also confirm that continuously adjusting control authority based on real-time deviations enhances vehicle stability while reducing driver effort during takeover.
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