Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
U.S. Army Ground Vehicle Systems Center, Warren, MI, USA.
Hum Factors. 2024 Apr;66(4):1235-1248. doi: 10.1177/00187208221129717. Epub 2022 Oct 7.
A human steering model for teleoperated driving is extended to capture the human steering behavior in haptic shared control of autonomy-enabled Unmanned Ground Vehicles (UGVs).
Prior studies presented human steering models for teleoperation of a passenger-sized Unmanned Ground Vehicle, where a human is fully in charge of driving. However, these models are not applicable when a human needs to interact with autonomy in haptic shared control of autonomy-enabled UGVs. How a human operator reacts to the presence of autonomy needs to be studied and mathematically encapsulated in a module to capture the collaboration between human and autonomy.
Human subject tests are conducted to collect data in haptic shared control for model development and validation. The ACT-R architecture and two-point steering model used in the previous literature are adopted to predict the operator's desired steering angle. A torque conversion module is developed to convert the steering command from the ACT-R model to human torque input, thus enabling haptic shared control with autonomy. A parameterization strategy is described to find the set of model parameters that optimize the haptic shared control performance in terms of minimum average lane keeping error (ALKE).
The model predicts the minimum ALKE human subjects achieve in shared control.
The extended model can successfully predict the best haptic shared control performance as measured by ALKE.
This model can be used in place of human operators, enabling fully simulation-based engineering, in the development and evaluation of haptic shared control technologies for autonomy-enabled UGVs, including control negotiation strategies and autonomy capabilities.
将人类遥控驾驶模型扩展到自主式无人地面车辆（UGV）触觉共享控制中的人类转向行为捕捉。
先前的研究提出了用于遥控载人尺寸的无人地面车辆的人类转向模型，在这种模型中，人类完全负责驾驶。然而，当人类需要在自主式无人地面车辆的触觉共享控制中与自主性进行交互时，这些模型并不适用。需要研究人类操作员如何对自主性的存在做出反应，并将其数学封装在一个模块中，以捕捉人类和自主性之间的协作。
进行人体主观测试，以收集触觉共享控制中的数据，用于模型开发和验证。采用先前文献中使用的 ACT-R 架构和两点转向模型来预测操作员的期望转向角度。开发了一个转矩转换模块，将来自 ACT-R 模型的转向命令转换为人类转矩输入，从而实现具有自主性的触觉共享控制。描述了一种参数化策略，以找到一组模型参数，这些参数可优化触觉共享控制在最小平均车道保持误差（ALKE）方面的性能。
该模型预测了人类在共享控制中实现的最小 ALKE。
扩展后的模型可以成功预测 ALKE 衡量的最佳触觉共享控制性能。
该模型可以替代人类操作员，使自主式无人地面车辆的触觉共享控制技术的开发和评估完全基于仿真，包括控制协商策略和自主性能力。
