  Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is
essential for ensuring safe and proactive navigation in autonomous driving.
Although cooperative perception has exceeded the detection capabilities of
single-agent systems, prevalent camera-based algorithms in cooperative
perception neglect valuable information derived from historical observations.
This limitation becomes critical during sensor failures or communication issues
as cooperative perception reverts to single-agent perception, leading to
degraded performance and incomplete BEV segmentation maps. This paper
introduces TempCoBEV, a temporal module designed to incorporate historical cues
into current observations, thereby improving the quality and reliability of BEV
map segmentations. We propose an importance-guided attention architecture to
effectively integrate temporal information that prioritizes relevant properties
for BEV map segmentation. TempCoBEV is an independent temporal module that
seamlessly integrates into state-of-the-art camera-based cooperative perception
models. We demonstrate through extensive experiments on the OPV2V dataset that
TempCoBEV performs better than non-temporal models in predicting current and
future BEV map segmentations, particularly in scenarios involving communication
failures. We show the efficacy of TempCoBEV and its capability to integrate
historical cues into the current BEV map, improving predictions under optimal
communication conditions by up to 2% and under communication failures by up to
19%. The code will be published on GitHub.
