SIGNATURE WORK
CONFERENCE & EXHIBITION 2024

This study introduces a pioneering approach to addressing latency in collaborative perception systems essential for applications like autonomous driving, where precision and timeliness are crucial. We propose an integration of Recurrent Convolutional Networks (RCNs) within a Time Modulation framework to mitigate latency effects. The RCNs leverage their strength in processing both spatial and temporal data, aiming to synchronize perception across collaborative agents dynamically. Though the anticipated computational experiments to validate the methodology were not fully realized, the theoretical implications suggest a substantial improvement in latency compensation over traditional models. This abstract encapsulates the envisioned enhancements in spatial-temporal feature integration, robustness to latency fluctuations, and the potential for synchronized feature alignment in collaborative tasks. Future work is directed towards empirical validation and optimization of the proposed framework for real-world applications.