A Markov Decision Process Model for a Reinforcement Learning-based Autonomous Pedestrian Crossing Protocol
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F21%3A00357837" target="_blank" >RIV/68407700:21260/21:00357837 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/MENACOMM50742.2021.9678310" target="_blank" >https://doi.org/10.1109/MENACOMM50742.2021.9678310</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MENACOMM50742.2021.9678310" target="_blank" >10.1109/MENACOMM50742.2021.9678310</a>
Alternative languages
Result language
angličtina
Original language name
A Markov Decision Process Model for a Reinforcement Learning-based Autonomous Pedestrian Crossing Protocol
Original language description
Autonomous Traffic Management (ATM) systems empowered with Machine Learning (ML) technics are a promising solution for eliminating traffic light and decreasing traffic congestion in the future. However, few efforts have focused on integrating pedestrians in ATM, namely the static programming-based cooperative protocol called Autonomous Pedestrian Crossing (APC). In this paper, we model a Markov Decision Process (MDP) to enable a Deep Reinforcement Learning (DRL)-based version of APC protocol that is able to dynamically achieve the same objectives (i.e. decreasing traffic delay at the crossing area). Using concrete state space, action set and reward functions, our model forces the Autonomous Vehicle (AV) to "think"and behave according to APC architecture. Compared to the traditional programming APC system, our approach permits the AV to learn from its previous experiences in non-signalized crossing and optimize the distance and the velocity parameters accordingly.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM)
ISBN
978-1-6654-3443-0
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
147-151
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Agadir
Event date
Dec 3, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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