Paper Info
Title
Semi-Definite Relaxation-Based ADMM for Cooperative Planning and Control of Connected Autonomous Vehicles
Abstract
This paper investigates the cooperative planning and control problem for multiple connected autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the methods suffer from significant problems in computational efficiency. Furthermore, as the optimization problem is nonlinear and nonconvex, it typically poses great difficulty in determining the optimal solution. To address this issue, this work proposes a novel and completely parallel computation framework by leveraging the alternating direction method of multipliers (ADMM). The nonlinear and nonconvex optimization problem in the autonomous driving problem can he divided into two manageable sub-problems; and the resulting sub-problems can be solved by using effective optimization methods in a parallel framework. Here, the differential dynamic programming (DDP) algorithm is capable of addressing the nonlinearity of the system dynamics rather effectively; and the nonconvex coupling constraints with small dimensions can be resolved by invoking the notion of semi-definite relaxation (SDR), which can also be solved in a very short time. Due to the parallel computation and efficient relaxation of nonconvex constraints, our proposed approach effectively realizes real-time implementation; and thus extra assurance of driving safety is provided. In addition, two transportation scenarios for multiple CAVs are used to illustrate the effectiveness and efficiency of the proposed method.
Year
DOI
Venue
2022
10.1109/TITS.2021.3094215
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Keywords
DocType
Volume
Autonomous driving, multi-agent system, model predictive control (MPC), alternative direction method of multipliers (ADMM), semi-definite relaxation (SDR), cooperative planning and control (CPaC)
Journal
23
Issue
ISSN
Citations 
7
1524-9050
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Xiaoxue Zhang100.34
Zilong Cheng202.03
Jun Ma300.34
Su-Nan Huang450561.65
Frank L. Lewis500.34
Tong Heng Lee600.34