One research aspect of the growing field of driver monitoring is the estimation of the driver's focus of attention and the detection of seen or missed surrounding objects. For this purpose, wearable head eye trackers, also called eye tracking glasses, are commonly used in research. For new Advanced Driver Assistance Systems, however, only remote gaze tracking systems in combination with series surround sensors (e. g. radar, camera) are a reasonable and suitable configuration. Remote eye trackers, though, do not reach the precision of wearable devices. Moreover, the use of inside-outside looking systems is affected by calibration errors. Both aspects lead to traffic participants classified as missed although they have been perceived by the driver and vice versa. Insight in the performance not only of the eye tracker itself but also on the model performance of an object of fixation detection algorithm, i. e. how a model handles those shortcomings, would be useful. In this work, we propose an approach to evaluate different models. For this, we combine the remote eye tracker with a wearable device, compare different models based on the newly obtained reference, and discuss the encountered problems.