<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>7403</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>357</pageFirst>
    <pageLast>364</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>9</volume>
    <type>article</type>
    <publisherName>De Gruyter</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Personalised neural networks for a driver intention prediction: communication as enabler for automated driving</title>
    <abstract language="eng">In everyday traffic, pedestrians rely on informal communication with other road users. In case of auto� mated vehicles, this communication can be replaced by&#13;
light signals, which need to be learned beforehand. Prior to an extensive introduction of automated vehicles, a learning phase for these light signals can be set up in manual driving with help of a driver intention prediction. Therefore, a three-staged algorithm consisting of a neural network, a random forest and a conditional stage, is implemented. Using this algorithm, a true-positive rate&#13;
(TPR) of 94.0% for a 5.0% false-positive rate (FPR) can be achieved. To improve this process, a personalization pro� cedure is implemented, using driver-specific behaviours, resulting in TPRs ranging from 91.5 to 96.6% for a FPR of 5.0%. Transfer learning of neural networks improves the prediction accuracy of almost all drivers. In order to introduce the implemented algorithm in today’s traffic,&#13;
especially the FPR has to be improved considerably.</abstract>
    <parentTitle language="eng">Advanced Optical Technologies</parentTitle>
    <identifier type="doi">10.1515/aot-2020-0035</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Johannes Reschke</author>
    <author>Cornelius Neumann</author>
    <author>Stephan Berlitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automotive lighting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning signals</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>recurrent neural networks</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>time sequence processing</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>vehicle-pedestrian-communication</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16315">Information und Kommunikation</collection>
  </doc>
  <doc>
    <id>7404</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>16</pageFirst>
    <pageLast>23</pageLast>
    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>121</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Communication between automated vehicles and pedestrians</title>
    <parentTitle language="eng">ATZ worldwide</parentTitle>
    <identifier type="doi">10.1007/s38311-019-0098-z</identifier>
    <enrichment key="opus.import.date">2024-08-24T03:59:05+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Johannes Reschke</author>
    <author>Marie-Theres Auburger</author>
    <author>Rebeca Marichalar</author>
    <author>Cornelius Neumann</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16315">Information und Kommunikation</collection>
  </doc>
</export-example>
