Conference paper Open Access

# Why did the human cross the road?

Panayiotis Charalambous; Yiorgos Chrysanthou

### DataCite XML Export

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<identifier identifierType="URL">https://zenodo.org/record/3747808</identifier>
<creators>
<creator>
<creatorName>Panayiotis Charalambous</creatorName>
<affiliation>Research Center on Interactive Media, Smart Systems and Emerging Technologies, Cyprus</affiliation>
</creator>
<creator>
<creatorName>Yiorgos Chrysanthou</creatorName>
<affiliation>Research Center on Interactive Media, Smart Systems and Emerging Technologies, Cyprus</affiliation>
</creator>
</creators>
<titles>
<title>Why did the human cross the road?</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2019</publicationYear>
<dates>
<date dateType="Issued">2019-10-01</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3747808</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3359566.3364696</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/rise-teaming-cyprus</relatedIdentifier>
</relatedIdentifiers>
<version>Published</version>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;&amp;lsquo;&amp;lsquo;Humans at rest tend to stay at rest. Humans in motion tend to cross the road &amp;ndash; Isaac Newton.&amp;rdquo; Even though this response is meant to be a joke to indicate the answer is quite obvious, this important feature of real world crowds is rarely considered in simulations. Answering this question involves several things such as how agents balance between reaching goals, avoid collisions with heterogeneous entities and how the environment is being modeled. As part of a preliminary study, we introduce a reinforcement learning framework to train pedestrians to cross streets with bidirectional traffic. Our initial results indicate that by using a very simple goal centric representation of agent state and a simple reward function, we can simulate interesting behaviors such as pedestrians crossing the road through crossings or waiting for cars to pass.&lt;/p&gt;</description>
<description descriptionType="Other">This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement  No 739578 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/739578/">739578</awardNumber>
<awardTitle>Research Center on Interactive Media, Smart System and Emerging Technologies</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

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