STCC: A Spatio-Temporal Calibration Method for Delay-Tolerant Cooperative Vehicular Network

Collaborative perception enhances situational awareness among vehicles by enabling information sharing across agents. However, network-induced delays reduce temporal consistency and introduce spatial divergence in shared observations, posing a critical challenge in distributed, delay-sensitive vehicular networks. Although delay alignment mitigates part of the effect, alignment errors and inherent multi-view discrepancies often lead to inconsistencies when synchronizing delayed data to a common timestamp. Crucially, these inconsistencies can accumulate during fusion, propagating cascading errors into downstream trajectory prediction. To address this, we propose STCC, a prediction-oriented collaborative perception framework based on Spatio-Temporal Co-Sensing Calibration. STCC adopts a temporal-spatial cascade design: a history-aware temporal alignment module captures dynamic alignment patterns to compensate for variable delays, while an Interaction-Aware Uncertainty Quantification scheme explicitly models environmental interaction risks to correct cross-view representation divergence. This joint calibration ensures consistent multi-agent fusion and reliable downstream prediction under network-induced delay. We evaluate STCC on large-scale public cooperative perception benchmarks. Experimental results show that STCC consistently outperforms state-of-the-art methods across diverse delay conditions, significantly reducing the Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior robustness and consistency.