Autonomous Intersection Management over Continuous Space: A Microscopic and Precise Solution via Computational Optimal Control

Abstract

Autonomous intersection management (AIM) refers to planning cooperative trajectories for multiple connected and automated vehicles (CAVs) when they pass through an unsignalized intersection. In modeling a generic AIM scheme, the predominant network-level or lane-level methods limit the cooperation potentiality of a multi-CAV team because 1) lane changes are forbidden or only allowed at discrete spots in the intersection, 2) each CAVs travel path is fixed or selected among a few topological choices, and 3) each CAVs travel velocity is fixed or set to a specified pattern. To overcome these limitations, this work models the intersection as a continuous free space and describes an AIM scheme as a multi-CAV trajectory optimization problem. Concretely, a centralized optimal control problem (OCP) is formulated and then numerically solved. To derive a satisfactory initial guess for the numerical optimization, a priority-based decentralized framework is proposed, wherein an x-y-time A* algorithm is adopted to generate a coarse trajectory for each CAV. To facilitate the OCP solution process, 1) the collision-avoidance constraints in the OCP are convexified, and 2) a stepwise computation strategy is adopted. Simulation results show the efficacy of the proposed offline AIM method.