High-resolution headlamps enable the generation of lane and symbol projections, offering additional information to the driver and other participants in traffic. While allowing the possibility of highlighting single pixels on the one hand, the light pattern without projections should give a smooth impression of the luminance distribution and should not show striking intensity differences between adjacent pixels. Which intensity differences lead to visible gaps in the light pattern is dependent on a set of numerous parameters. A model for predicting the visibility of such intensity gaps for human observers is proposed here, a task which is directly linked to contrast detection. Hence the deployed model implements sequentially applied sub models based on the contrast sensitivity function (CSF). In order to evaluate the model, it is tested with luminance distributions of a simulated pixelated light source as well as with a more complex scenario. First evaluations of the model show promising results. For each tested scenario the model behaves as expected. The validity of the model will have to be verified by a study in a next step.