Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception. Existing collaborative perception methods usually consider an ideal communication environment. However, in practice, the communication system inevitably suffers from latency issues, causing potential performance degradation and high risks in safety-critical applications, such as autonomous driving. To mitigate the effect caused by the inevitable latency, from a machine learning perspective, we present the first latency-aware collaborative perception system, which actively adapts asynchronous perceptual features from multiple agents to the same time stamp, promoting the robustness and effectiveness of collaboration. To achieve such a feature-level synchronization, we propose a novel latency compensation module, called SyncNet, which leverages feature-attention symbiotic estimation and time modulation techniques. Experiments results show that the proposed latency aware collaborative perception system with SyncNet can outperforms the state-of-the-art collaborative perception method by 15.6% in the communication latency scenario and keep collaborative perception being superior to single agent perception under severe latency.
翻译:现有的协作性认知方法通常考虑理想的通信环境,但在实践中,通信系统不可避免地会遇到潜伏问题,造成潜在的性能退化和安全关键应用(如自主驱动)的高风险。为了减轻不可避免的潜伏所造成的影响,我们从机器学习的角度介绍了第一个潜伏-自觉协作认知系统,该系统积极将多个代理人的非同步感知特征调整为同一时间标记,促进协作的稳健性和有效性。为了实现这种特征级同步,我们提议了一个称为SyoncNet的新型延缓性补偿模块,该模块利用地势注意共生估计和时间调节技术。实验结果表明,拟议的潜伏性意识与同步网络的合作性认知系统可以在通信隐蔽情景下使最先进的协作性认知方法超过15.6%,并使协作性认知优于单一代理人在严重悬浮状态下的看法。