Context-Aware Timely Status Updates for Trajectory Control With Limited Communication Resources

Advances in information and control technology act as enablers for the utilization of Connected Autonomous Vehicles (CAVs). Despite the extensive research on trajectory control, most investigations assume that either the communication process is perfect or CAVs know their exact location and system state. To this end, we propose a novel trajectory control scheme that allows a Centralized Manager (CM) to account for the limited communication resources and trajectory uncertainty due to vehicle state evolution errors and measurement errors. In particular, the scheme includes covariance-based context-aware timely status update strategy optimization using Kalman estimation techniques and robust trajectory control using the recently developed theory of covariance steering. Moreover, the original stochastic trajectory control problem under non-convex feasible regions is converted to a deterministic mixed integer programming (MIP) problem in terms of the accessible estimated state. Simulation results illustrate the effectiveness and robustness of the proposed scheme and reveal the impact of the different update frequencies on the trajectory.