Improved model-free adaptive predictive control-based cooperative driving control for connected and automated vehicles subject to time-varying communication delays and packet losses at signal-free intersections

In a vehicle-to-infrastructure (V2I) communication network at signal-free intersections, time-varying delays and random packet losses may occur because of the unreliable nature of wireless communication, which can degrade the performance of cooperative driving for large-scale connected and automated vehicles (CAVs). Consequently, to solve this problem, this paper proposed a cooperative driving control method based on improved model-free adaptive predictive control (IMFAPC). I/O data of the target CAVs during driving are adopted to realize the networked predictive control of the time-varying transmission delays and packet losses in the dynamic linearization prediction scheme and moving horizon prediction control technology. And then the vehicle can be allocated the expected speed to realize accurate vehicle control. In addition, the convergence of the IMFAPC-based cooperative driving control algorithm is proven by conducting a stability analysis. Extensive numerical experiments verified the effectiveness of the proposed control strategy, and the results demonstrate that the proposed method can alleviate the adverse effects of time-varying delays and random packet losses on the control effects, thereby improving traffic efficiency under various traffic volumes on the basis of ensuring the safety of multi-vehicle driving.