Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

• Published in: Computer-aided civil and infrastructure engineering Vol. 36; no. 7; pp. 838 - 857
• Main Authors: Chen, Sikai, Dong, Jiqian, Ha, Paul (Young Joun), Li, Yujie, Labi, Samuel
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.07.2021
• Subjects: Algorithms; Artificial neural networks; Control algorithms; Control theory; Convolution; Cooperative control; Data integration; Graph neural networks; Machine learning; Microprocessors; Neural networks; Roads; Roadsides; Safety; Traffic information; Vehicles
• ISSN: 1093-9687; 1467-8667
• DOI: 10.1111/mice.12702

A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high‐level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast‐growing joint action space associated with multi‐agent driving tasks pose difficultly in achieving cooperative control. The problem is NP‐hard and cannot be efficiently resolved using rule‐based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning‐based algorithm that combines graphic convolution neural network with deep Q‐network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi‐lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane‐changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis‐à‐vis traditional rule‐based and long short‐term memory‐based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane‐change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.