Search

Workload management is a critical concern in shared control of unmanned ground vehicles. In response to this challenge, prior studies have developed methods to estimate human operators' workload by analyzing their physiological data. However, these studies have primarily adopted a single-model-single-feature or a single-model-multiple-feature approach. The present study proposes a Bayesian inference model to estimate workload, which leverages different machine learning models for different features. We conducted a human subject experiment with 24 participants, in which a human operator teleoperated a simulated High Mobility Multipurpose Wheeled Vehicle (HMMWV) with the help from an autonomy while performing a surveillance task simultaneously. Participants' eye-related features, including gaze trajectory and pupil size change, were used as the physiological input to the proposed Bayesian inference model. Results show that the Bayesian inference model achieves a 0.823 F1 score, 0.824 precision, and 0.821 recall, outperforming the single models. [ABSTRACT FROM AUTHOR]

Objective: 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). Background: 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. Method: 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). Results: The model predicts the minimum ALKE human subjects achieve in shared control. Conclusions: The extended model can successfully predict the best haptic shared control performance as measured by ALKE. Application: 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. [ABSTRACT FROM AUTHOR]