부산대학교 도서관

This article proposes a distributed control methodology for the coordination of multiple connected and automated vehicles (CAVs). Each vehicle computes its local trajectory based on a model predictive control (MPC) law and communicates the result with other relevant vehicles. In an iterative negotiation process, planned trajectories are optimized within a sampling time step. Inspired by the Jacobi over-relaxation (JOR) algorithm, we develop a distributed Jacobi over-relaxation algorithm (DJOR) for vehicle coordination. The modified algorithm exploits the structure of the distributed problem setting in which coupling occurs only in a bilateral way. Besides being able to guarantee any-time feasibility that implies collision-freeness, the algorithm scales well, unlike the standard JOR algorithm. The DJOR algorithm allows for significantly less conservatism in the choice of the update weightings. As a result, much faster convergence rates can be expected. Furthermore, the collision avoidance guarantee is extended for unforeseen scenarios such as emergency braking. Using an exact penalty function formulation ensures that the distributed optimization problem remains feasible even in previously unforeseen cases. Numerical simulations of an intersection crossing scenario illustrate the presented approach and show its benefits in comparison with standard traffic rules and a centralized computation.