AoI Analysis of RIS-Assisted Vehicular Networks and the Impact on Cooperative Maneuvers

Cooperative, Connected, and Automated Mobility (CCAM) is based on fast, secure, and reliable Vehicle-to-Everything (V2X) communication to enable collective perception and maneuver coordination in autonomous driving. However, high-frequency wireless communication, particularly in the millimeter wave (mmWave) and terahertz (THz) bands, is highly susceptible to environmental obstacles, leading to severe signal attenuation and communication delays. This study investigates the integration of Reconfigurable Intelligent Surfaces (RIS) into vehicular networks to mitigate these challenges and analyzes their impact on Age of Information (AoI) and Peak AoI (PAoI) metrics. The latter metrics are highly relevant to feedback delays introduced in the Cooperative Control Schemes of Autonomous Vehicles, which are well known to compromise their stability. Their characterization is thus crucial for the performance assessment of the automatic controllers with this characterization in the presence of RIS not investigated as of now in the literature. Using the All-or-Nothing Receiver Model (ANRM) and Distance-Dependent Propagation Model (DDPM), we extend AoI formulations by incorporating RIS path loss characteristics revealing that RIS-assisted networks significantly reduce AoI and PAoI values, enabling reliable cooperative control in vehicular systems. We consider a cooperative merging maneuver on an intersection as our test case and demonstrate that the effect of the introduced delays on the cooperative control scheme performance is minimal. Passive RISs are assumed throughout the study, due to their energy efficient operation as compared to active metasurfaces.