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

• Published in: IFAC-PapersOnLine Vol. 53; no. 2; pp. 17071 - 17076
• Main Authors: Li, Bai, Zhang, Youmin, Jia, Ning, Peng, Xiaoyan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2020
• Subjects: automated vehicles (CAVs); Autonomous intersection management (AIM); computational optimal control; connected; numerical optimization; trajectory planning; connected; Autonomous intersection management (AIM); automated vehicles (CAVs); computational optimal control; numerical optimization; trajectory planning
• ISSN: 2405-8963; 2405-8971; 2405-8963
• DOI: 10.1016/j.ifacol.2020.12.1611

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.