Humans and intelligent machines increasingly collaborate on complex tasks, although significant challenges remain before machines can function as effective teammates. The human-machine teaming research community attempts to address these challenges by developing and testing methods that identify and enhance the factors essential for successful teaming. However, this community suffers from a lack of requirements for effective research, numerous methods without centralized documentation, and a disconnect between research and real-world applications. These challenges hinder progress and limit the generalizability of research outcomes. To address these issues, we argue that the human-machine teaming research community should establish a more structured and systematic approach to studying and advancing the field. This paper identifies and discusses several key research directions and actionable outputs for such an approach. These include taxonomies and guidelines to streamline research, team design patterns to describe reusable solutions, modular testbeds to facilitate comparability and reuse, and study templates to foster creativity and encourage sharing. We believe that these elements can help formulate requirements for effective human-machine teaming research and foster the development of modular and well-documented testbeds. Achieving these goals can contribute to more ecologically valid human-machine teaming research and, thus, a stronger connection between research and real-world applications.

Situation awareness (SA) is critical to improving takeover performance during the transition period from automated driving to manual driving. Although many studies measured SA during or after the driving task, few studies have attempted to predict SA in real time in automated driving. In this work, we propose to predict SA during the takeover transition period in conditionally automated driving using eye-tracking and self-reported data. First, a tree ensemble machine learning model, named LightGBM (Light Gradient Boosting Machine), was used to predict SA. Second, in order to understand what factors influenced SA and how, SHAP (SHapley Additive exPlanations) values of individual predictor variables in the LightGBM model were calculated. These SHAP values explained the prediction model by identifying the most important factors and their effects on SA, which further improved the model performance of LightGBM through feature selection. We standardized SA between 0 and 1 by aggregating three performance measures (i.e., placement, distance, and speed estimation of vehicles with regard to the ego-vehicle) of SA in recreating simulated driving scenarios, after 33 participants viewed 32 videos with six lengths between 1 and 20 s. Using only eye-tracking data, our proposed model outperformed other selected machine learning models, having a root-mean-squared error (RMSE) of 0.121, a mean absolute error (MAE) of 0.096, and a 0.719 correlation coefficient between the predicted SA and the ground truth. The code is available at https://github.com/refengchou/Situation-awareness-prediction. Our proposed model provided important implications on how to monitor and predict SA in real time in automated driving using eye-tracking data.