Enhancing truck platooning efficiency and safety—A distributed Model Predictive Control approach for lane-changing manoeuvres

Abstract

The advent of autonomous driving technologies has paved the way for notable advancements in the realm of transportation systems. This paper explores the dynamic field of truck platooning, focusing on the development of a Nonlinear Model Predictive Control (NMPC) approach within a Cooperative Adaptive Cruise Control (CACC) framework. The research tackles the critical challenges in obstacle avoidance and lane-changing manoeuvres. The core contribution of this work lies in the development and implementation of a novel NMPC algorithm tailored to platoon control. This framework integrates a penalty soft constraint to guarantee obstacle avoidance and maintain platoon coherence while optimising control inputs in real-time. Several experiments, including static and dynamic obstacle avoidance scenarios, validate the efficacy of the proposed approach. In all experiments, the vehicles closely follow one another, resulting in smooth trajectories for all system states and control input signals. Even in the event of abrupt braking by the ego vehicle, the platoon remains cohesive. Moreover, the proposed NMPC proves to be computationally efficient when compared to the state-of-the-art.