Distributed Trajectory Optimization for Connected and Automated Vehicle Platoons Considering Safe Inter-Vehicle Following Gaps - Meiqi Liu; Ying Gao; Yikai Zeng; Ruochen Hao (Systems, 2025)
Existing studies on platoon trajectory optimization of connected and automated vehicles face challenges in balancing computational efficiency, privacy, and safety. This study proposes a distributed optimization method that decomposes the platoon trajectory planning problem into independent individual vehicle tasks while ensuring safe inter-vehicle following gaps and maximizing travel efficiencyand ride comfort. The individual vehicle problems independently optimize their trajectory to improve computational efficiency, and only exchange dual variables related to safe following gaps to preserve privacy. Simulation experiments were conducted under single-platoon scenarios with different simulation horizons, as well as multi-platoon and platoon-merging scenarios, to analyze the control performance of the distributed method in contrast to the centralized method. Simulation results demonstrate that the mean computation time is reduced by 50% and the fuel consumption is decreased by 4% compared to the centralized method while effectively maintaining the safe inter-vehicle following gaps. The distributed method shows its scalability and adaptability for large-scale problems.