Multi-Agent Deep Reinforcement Learning for Traffic optimization through Multiple Road Intersections using Live Camera Feed

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

Traffic signals provide one of the primary means to administer conflicting traffic flows. Existing signal control strategies, operating on hand-crafted rules, fail to efficiently, autonomously adapt to the changing traffic patterns. Each signal control system independently manages one intersection at a time and regulates navigation of vehicles through that intersection. Current systems cannot co-operate to optimize aggregate traffic flows through multiple road intersections. Consequently, they are susceptible to making myopic signal control decisions that might be effective locally, but not globally. Instead, we propose a system of multiple, coordinating traffic signal control systems. This paper presents the first application of multi-agent deep reinforcement learning (DRL) to achieve traffic optimization through multiple road intersections solely based on raw pixel input from CCTV cameras in real time. This set of traffic control agents is shown to significantly outperform independently operating (both DRL-trained and loop-induced) adaptive signal control systems, by increasing traffic throughput and reducing the average time a vehicle spends in an intersection. Additionally, this paper, introduces attention-based visualization to interpret and validate the proposed multi-agent signal control methodology.

Original languageEnglish
Title of host publication2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PublisherIEEE
ISBN (Electronic)9781728141497
DOIs
Publication statusPublished - 24 Dec 2020
Event23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece
Duration: 20 Sep 202023 Sep 2020

Publication series

Name2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

Conference

Conference23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
CountryGreece
CityRhodes
Period20/09/2023/09/20

Bibliographical note

© 2020 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.

Fingerprint Dive into the research topics of 'Multi-Agent Deep Reinforcement Learning for Traffic optimization through Multiple Road Intersections using Live Camera Feed'. Together they form a unique fingerprint.

  • A Deep Reinforcement Learning Agent for Traffic Intersection Control Optimization

    Garg, D., Chli, M. & Vogiatzis, G., 28 Nov 2019, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. IEEE, p. 4222-4229 8 p. 8917361

    Research output: Chapter in Book/Report/Conference proceedingConference publication

  • Traffic3D: A new traffic simulation paradigm

    Garg, D., Chli, M. & Vogiatzis, G., 8 May 2019, 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019. ACM, p. 2354-2356 3 p. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; vol. 4).

    Research output: Chapter in Book/Report/Conference proceedingConference publication

    Open Access
    File

Cite this