Real-time masking of vehicles in a dynamic road environment is a demanding task for adaptive driving beam systems of modern headlights. Next-generation high-density matrix headlights enable precise, high-resolution projections, while advanced driver assistance systems enable detection and tracking of objects with high update rates and low-latency estimation of the pose of the ego-vehicle. Accurate motion tracking and precise coverage of the masked vehicles are necessary to avoid glare while maintaining a high light throughput for good visibility. Safety margins are added around the mask to mitigate glare and flicker caused by the update rate and latency of the system. We provide a model to estimate the effects of spatial and temporal sampling on the safety margins for high- and low-density headlight resolutions and different update rates. The vertical motion of the ego-vehicle is simulated based on a dynamic model of a vehicle suspension system to model the impact of the motion-to-photon latency on the mask. Using our model, we evaluate the light throughput of an actual matrix headlight for the relevant corner cases of dynamic masking scenarios depending on pixel density, update rate, and system latency. We apply the masks provided by our model to a high beam light distribution to calculate the loss of luminous flux and compare the results to a light throughput approximation technique from the literature.