Collaborative Lane Change of Heterogeneous Truck Platoon Based on MEC Control Architecture With Constant Communication Delay

Truck platoons are expected to play a key role in the future of cargo transportation, while high-speed cooperative lane changes pose significant risks. This paper introduces a collaborative lane change control (CLCC) framework for truck platoons that utilizes a multi-access edge computing (MEC) control architecture. A parallel signal superposition method is presented, which has doubled the control signal refresh frequency. Additionally, an improved nonlinear articulated model predictive control (NA-MPC) algorithm, which accounts for the nonlinear articulated characteristics of trucks, is proposed and deployed on the roadside and on-board equipment to form a key component of the CLCC framework. A cooperative adaptive cruise control (CACC) algorithm based on proportional-integral (PI) and proportional-derivative (PD) controllers is also adopted for longitudinal following. Simulation results demonstrate that the signal superposition approach reduces lateral deviations, and the improved NA-MPC algorithm enhances the robustness of the platoon during emergency maneuvers. The CLCC framework even effectively minimizes maximum lateral deviations under the shortest path$ S\_{53}$. Furthermore, the CACC algorithm ensures the string stability. These results validate the effectiveness of the proposed methods, particularly in the presence of constant communication delays, highlighting their potential to improve the stability of cooperative lane changes within truck platoons.