Maintaining situation awareness (SA) is essential for drivers to deal with the situations that Society of Automotive Engineers (SAE) Level 3 automated vehicle systems are not designed to handle. Although advanced physiological sensors can enable continuous SA assessments, previous single-modality approaches may not be sufficient to capture SA. To address this limitation, the current study demonstrates a multimodal sensing approach for objective SA monitoring. Physiological sensor data from electroencephalogram and eye-tracking were recorded for 30 participants as they performed three secondary tasks during automated driving scenarios that consisted of a pre-takeover (pre-TOR) request segment and a post-TOR segment. The tasks varied in terms of how visual attention was allocated in the pre-TOR segment. In the post-TOR segment, drivers were expected to gather information from the driving environment in preparation for a vehicle-to-driver transition. Participants' ground-truth SA level was measured using the Situation Awareness Global Assessment Techniques (SAGAT) after the post-TOR segment. A total of 23 physiological features were extracted from the post-TOR segment to train computational intelligence models. Results compared the performance of five different classifiers, the ground-truth labeling strategies, and the features included in the model. Overall, the proposed neural network model outperformed other machine learning models and achieved the best classification accuracy (90.6%). A model with 11 features was optimal. In addition, the multi-physiological sensor-model outperformed the single sensing model by comparing prediction performance. Our results suggest that multimodal sensing model can objectively predict SA. The results of this study provide new insight into how physiological features contribute to the SA assessment.

Research indicates that monotonous automated driving increases the incidence of fatigued driving. Although many prediction models based on advanced machine learning techniques were proposed to monitor driver fatigue, especially in manual driving, little is known about how these black-box machine learning models work. In this paper, we proposed a combination of Gaussian Process Boosting (GPBoost) and SHapley Additive exPlanations (SHAP) to predict driver fatigue with explanations. First, in order to obtain the ground truth of driver fatigue, we used PERCLOS (percentage of eyelid closure over the pupil over time) between 0 and 100 as the response variable. Second, we built a driver fatigue regression model using both physiological and behavioral measures with GPBoost that was able to address the within-subjects correlations. This model outperformed other selected machine learning models with root-mean-squared error (RMSE) = 2.965, mean absolute error (MAE) = 1.407, and adjusted R2=0.996. Third, we employed SHAP to identify the most important predictor variables and uncovered the black-box GPBoost model by showing the main effects of the most important predictor variables globally and explaining individual predictions locally. Such an explainable driver fatigue prediction model offered insights into how to intervene in automated driving when necessary, such as during the takeover transition period from automated driving to manual driving.

Pole-zero approximations of human free-field-to-eardrum transfer functions-i.e., head-related transfer functions (HRTFs)-can be used to synthesize binaural acoustic fields over headphones with fewer parameters, and therefore, lower computational complexity than is typically achieved by all-zero approximations. While standard pole-zero designs minimize the squared difference between the desired and designed frequency responses, the synthesis of binaural acoustic fields is more likely to be sensitive to relative, rather than absolute, differences in the HRTF approximations. The paper proposes a new design algorithm that minimizes an error criterion based on complex logarithms. As a result, both log-magnitude and phase errors are minimized. Slight modifications to the proposed algorithm result in one for which the phase error can be ignored. A comparison of this modified algorithm to others that have been developed to minimize the log-magnitude error shows implementation advantages without sacrificing performance. In achieving comparable errors, application of the proposed algorithm to the HRTF approximation results in pole-zero model orders smaller than those required for approximating the HRTFs using a least-squares error criterion.