Autonomous Intersection Management over Continuous Space: A Microscopic and Precise Solution via Computational Optimal Control. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Li, Bai; Zhang, Youmin; Jia, Ning; Peng, Xiaoyan
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 17071
• Page End: 17076
• Publication Date: 2020
• Subjects: Autonomous intersection management (AIM) -- connected -- automated vehicles (CAVs) -- trajectory planning -- numerical optimization -- computational optimal control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1611
• Languages: English
• ISSNs: 2405-8963

Abstract

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.