Imperial College London
Alternate

Panagiotis Angeloudis

Faculty of Engineering, Department of Civil and Environmental Engineering

Reader in Transport Systems and Logistics
 
 
 
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Contact

 

+44 (0)20 7594 5986p.angeloudis Website

 
 
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Location

 

337Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ye:2023:10.1109/tits.2022.3220110,
author = {Ye, Q and Feng, Y and Macias, JJE and Stettler, M and Angeloudis, P},
doi = {10.1109/tits.2022.3220110},
journal = {IEEE Transactions on Intelligent Transportation Systems},
pages = {2024--2034},
title = {Adaptive road configurations for improved autonomous vehicle-pedestrian interactions using reinforcement learning},
url = {http://dx.doi.org/10.1109/tits.2022.3220110},
volume = {24},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Ye,Q
AU  - Feng,Y
AU  - Macias,JJE
AU  - Stettler,M
AU  - Angeloudis,P
DO  - 10.1109/tits.2022.3220110
EP  - 2034
PY  - 2023///
SN  - 1524-9050
SP  - 2024
TI  - Adaptive road configurations for improved autonomous vehicle-pedestrian interactions using reinforcement learning
T2  - IEEE Transactions on Intelligent Transportation Systems
UR  - http://dx.doi.org/10.1109/tits.2022.3220110
UR  - https://scholar.google.co.uk/citations?user=7haYvj8AAAAJ&hl=en
UR  - https://ieeexplore.ieee.org/document/9946858
UR  - http://hdl.handle.net/10044/1/100892
VL  - 24
ER  -
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