Adaptive road configurations for improved autonomous vehicle-pedestrian interactions using reinforcement learning
Author(s)
Ye, Qiming
Feng, Yuxiang
Macias, Jose Javier Escribano
Stettler, Marc
Angeloudis, Panagiotis
Type
Journal Article
Abstract
The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55%), benchmark rewards (25.35%), best cumulative rewards (24.58%), optimal actions (13.49%) and rate of convergence. This novel road management technique could potentially contribute to the flow-adaptive and active mobility-friendly streets in the AVs era.
Date Issued
2023-02
Date Acceptance
2022-11-01
Citation
IEEE Transactions on Intelligent Transportation Systems, 2023, 24 (2), pp.2024-2034
ISSN
1524-9050
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
2024
End Page
2034
Journal / Book Title
IEEE Transactions on Intelligent Transportation Systems
Volume
24
Issue
2
Copyright Statement
Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://scholar.google.co.uk/citations?user=7haYvj8AAAAJ&hl=en
Subjects
Infrastructure management
Autonomous vehicles
Pedestrians
smart city
Intelligent Transportation Systems
Reinforcement Learning
Publication Status
Published
Coverage Spatial
United Kingdom
Date Publish Online
2022-11-12