In automated driving, it is important to maintain drivers’ situational awareness (SA) in order to help them avoid unnecessary interventions and negotiate challenging scenarios where human takeovers are needed. Our study developed computational models to predict a driver’s SA of a target object. Using the SEEV (Salience, Effort, Expectancy, and Value) and ACT-R (Adaptive Control of Thought-Rational) framework, the model achieved an accuracy of 78.3%, an F1-score of 0.66, and the area under the receiver operating characteristic (AUROC) value of 0.773 with object features as inputs. On average, the model had a Root Mean Square Error (RMSE) of 0.18 to predict the SA of a target object across participants. In relative to the existing models, our model not only had comparable predictive performance but also considered the underlying mechanism of SA to increase model interpretability. Our research will provide essential and necessary steps toward developing in-vehicle SA prediction and assistance systems.