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.

翻译：准确且全面的鸟瞰图语义分割对于确保自动驾驶中安全且主动的导航至关重要。尽管协同感知已超越单智能体系统的检测能力，但协同感知中基于摄像头的流行算法忽略了从历史观测中获取的宝贵信息。在传感器故障或通信问题期间，这一局限变得关键，因为协同感知会退化为单智能体感知，导致性能下降和不完整的鸟瞰图分割图。本文提出TempCoBEV，一个时间模块，旨在将历史线索融入当前观测，从而提升鸟瞰图分割的质量与可靠性。我们提出一种基于重要性引导的注意力架构，以有效整合优先关注鸟瞰图分割相关属性的时间信息。TempCoBEV是一个独立的时间模块，可无缝集成至最先进的基于摄像头的协同感知模型中。通过在OPV2V数据集上的广泛实验，我们证明TempCoBEV在预测当前及未来鸟瞰图分割方面优于非时间模型，尤其在涉及通信故障的场景中。我们展示了TempCoBEV的有效性及其将历史线索融入当前鸟瞰图的能力，在最佳通信条件下预测提升高达2%，在通信故障条件下提升高达19%。代码将发布于GitHub。