Optimal control-based online motion planning for cooperative lane changes of connected and automated vehicles

Abstract

This work formulates the multi-vehicle lane change motion planning task as a centralized optimal control problem, which is beneficial in being generic and complete. However, a direct solution to this optimal control problem is numerically intractable due to the dimensionality of the collision-avoidance constraints and nonlinearity of the vehicle kinematics. A progressively constrained dynamic optimization (PCDO) method is proposed to facilitate the numerical solving process of this complicated problem. PCDO guarantees to efficiently obtain an optimum to the original optimal control problem via solving a sequence of simplified problems which gradually judge and reserve only the active collision-avoidance constraints. A first-regularization-then-action strategy, together with the look-up table technique, is developed for online solutions. At the regularization stage, the vehicles form a standard formation by linear acceleration/deceleration only. At the action stage, the vehicles execute lane change motions computed offline and recorded in the look-up table. This makes online motion planning feasible because 1) the computational complexity at the regularization stage scales linearly rather than exponentially with the vehicle number; and 2) online computation at the action stage is fully avoided through data extraction from the look-up table.