  In this work, we present a novel strategy for correcting imperfections in
occupancy grid maps called map decay. The objective of map decay is to correct
invalid occupancy probabilities of map cells that are unobservable by sensors.
The strategy was inspired by an analogy between the memory architecture
believed to exist in the human brain and the maps maintained by an autonomous
vehicle. It consists in merging sensory information obtained during runtime
(online) with a priori data from a high-precision map constructed offline. In
map decay, cells observed by sensors are updated using traditional occupancy
grid mapping techniques and unobserved cells are adjusted so that their
occupancy probabilities tend to the values found in the offline map. This
strategy is grounded in the idea that the most precise information available
about an unobservable cell is the value found in the high-precision offline
map. Map decay was successfully tested and is still in use in the IARA
autonomous vehicle from Universidade Federal do Esp\'irito Santo.
