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.

通过将历史线索整合到当前 BEV 地图中，TempCoBEV 能够在协作感知中改进 BEV 地图分割的质量和可靠性。在 OPV2V 数据集上进行的广泛实验表明，TempCoBEV 在预测当前和未来的 BEV 地图分割方面比非时态模型表现更好，特别是在涉及通信故障的情景中，其整合历史线索到当前 BEV 地图的能力使预测结果在最佳通信条件下提高 2%，在通信故障下提高 19%。