Temporal Assembly of Received Regional Features for Collaborative Perception under Coupled Delay and Loss

Collaborative perception extends an ego vehicle’s view using observations from neighboring vehicles, but depends on timely communication. When vehicle-tovehicle (V2V) communication degrades, delay and packet loss can jointly leave a neighbor’s current update incomplete or unavailable at a perception deadline, potentially reducing detection accuracy. To study this phenomenon, we introduce CoDeV2V, a newly collected long-sequence multi-vehicle LiDAR dataset. CoDeV2V models each V2V link with a condition—good, bad, or outage—that can persist across updates. The link condition jointly affects transmission delay and loss; an outage makes the affected neighbor’s update unavailable and may block several consecutive updates if it persists. Consequently, current neighbor information may be incomplete or unavailable at the perception deadline, while retained information becomes stale. Existing methods address aspects of communication degradation, but on CoDeV2V the evaluated methods still lose substantial detection accuracy compared with complete and timely neighbor information. To mitigate the remaining loss in detection accuracy, we propose AssemblyFusion. AssemblyFusion assembles regional features received from current and earlier observations, using how old the information is and where it is available to guide fusion with ego features. The ego vehicle also selects detections from each neighbor’s latest available observation, updates older detections using motion estimates, and combines the selected detections with predictions from fused features. Together, regional feature assembly uses the portions of neighbor updates that arrive by the deadline, while motion compensation updates historical detections when a neighbor’s current detections are unavailable, mitigating the impact of coupled delay and loss on perception. Under severe coupled communication degradation on the 5,400-frame CoDeV2V test split, AssemblyFusion achieves 53.20 ± 0.46 BEV [email protected] across three communication realizations, 6.70 points above CoAlign. Ablations show higher accuracy with motion-compensated historical detections than with discarded or uncorrected historical detections.