ADCC: AoI-aware Decentralized Congestion Control in Cooperative Perception System

Cooperative perception, based on vehicle-to-everything (V2X) communication technology, is a promising solution for connected and automated vehicles (CAVs) to improve their perception capabilities in intelligent transportation systems. The frequency of message transmission in cooperative perception among mobile vehicles plays a crucial role, as it directly impacts communication efficiency, perception accuracy, system response speed, and the safety of real-time applications. Higher transmission frequencies can provide more timely and rich sensory information. This also implies higher consumption of communication resources. However, in a dynamic and complex environment, it is difficult to quantitatively control the message transmission frequency so as to improve the utilization of limited communication resources, ensure the timeliness of perception messages, and maintain fast convergence. To address challenges of timeliness of messages, a timeliness performance metric age of information (AoI) is introduced to control message transmission frequency. This paper deduced the average AoI of the system and designed an AoI-aware decentralized congestion control (ADCC) algorithm for the V2X-based cooperative perception system. Simulation results show that the ADCC algorithm outperforms the classic congestion control algorithm linear adaptive message rate (LIMERIC) in terms of channel utilization and throughput. Specifically, AoI decreased by 47.3%, channel utilization increased by 6.5% and throughput increased by 48.6%.