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    <title language="eng">A Holistic View on Probabilistic Trajectory Forecasting – Case Study. Cyclist Intention Detection</title>
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    <abstract language="eng">In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available.</abstract>
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    <title language="eng">Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks</title>
    <abstract language="eng">In this work, we use Recurrent Neural Networks (RNNs) in form of Gated Recurrent Unit (GRU) networks to forecast trajectories of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic utilizing the past trajectory and 3D poses as input. The 3D poses represent the postures and movements of limbs and torso and contain early indicators for the transition between motion types, e.g. wait, start, move, and stop. VRUs often only become visible from the perspective of an approaching vehicle shortly before dangerous situations occur. Therefore, a network architecture is required which is able to forecast trajectories after short time periods and is able to improve the forecasts in case of longer observations. This motivates us to use GRU networks, which are able to use time series of varying duration as inputs, and to investigate the effects of different observation periods on the forecasting results. Our approach is able to make reasonable forecasts even for short observation periods. The use of poses improves the forecasting accuracy, especially for short observation periods compared to a solely head trajectory based approach. Different motion types benefit to different extent from the use of poses and longer observation periods.</abstract>
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      <value>Fußgänger</value>
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      <value>Radfahrer</value>
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    <title language="eng">Cyclists starting behavior at intersections</title>
    <parentTitle language="eng">2017 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="doi">10.1109/IVS.2017.7995856</identifier>
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    <author>Andreas Hubert</author>
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      <value>Fahrerassistenzsystem</value>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-10-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Model-predictive planning for autonomous vehicles anticipating intentions of vulnerable road users by artificial neural networks</title>
    <parentTitle language="eng">2017 IEEE Symposium Series on Computational Intelligence (SSCI)</parentTitle>
    <identifier type="doi">10.1109/SSCI.2017.8285249</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Jan Eilbrecht</author>
    <author>Maarten Bieshaar</author>
    <author>Stefan Zernetsch</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <author>Olaf Stursberg</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fußgänger</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
  <doc>
    <id>1696</id>
    <completedYear>2017</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>8</pageLast>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Yokohama, Japan</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-10-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cooperative Starting Intention Detection of Cyclists based on Smart Devices and Infrastructure</title>
    <parentTitle language="eng">2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="doi">10.1109/ITSC.2017.8317691</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Maarten Bieshaar</author>
    <author>Stefan Zernetsch</author>
    <author>Malte Depping</author>
    <author>Bernhard Sick</author>
    <author>Konrad Doll</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
  <doc>
    <id>1677</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3035</pageFirst>
    <pageLast>3045</pageLast>
    <pageNumber/>
    <edition/>
    <issue>21 / 7</issue>
    <volume>2020</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-06-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Intentions of Vulnerable Road Users – Detection and Forecasting by Means of Machine Learning</title>
    <parentTitle language="eng">IEEE Transactions on Intelligent Transportation Systems</parentTitle>
    <identifier type="doi">10.1109/TITS.2019.2923319</identifier>
    <identifier type="url">https://doi.org/10.1109/TITS.2019.2923319</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Michael Goldhammer</author>
    <author>Sebastian Köhler</author>
    <author>Stefan Zernetsch</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <author>Klaus Dietmayer</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fußgänger</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
  <doc>
    <id>1500</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Changshu, China</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-10-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network</title>
    <abstract language="eng">In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks. By combining MHIs with a ResNet classifier and performing a frame by frame classification of the MHIs, we are able to detect starting motions in image sequences. The detection is performed using a wide angle stereo camera system at an urban intersection. We compare our algorithm to an existing method to detect movement transitions of pedestrians that uses MHIs in combination with a Histograms of Oriented Gradients (HOG) like descriptor and a Support Vector Machine (SVM), which we adapted to cyclists. To train and evaluate the methods a dataset containing MHIs of 394 cyclist starting motions was created. The results show that both methods can be used to detect starting motions of cyclists. Using the SVM approach, we were able to safely detect starting motions 0.506 s on average after the bicycle starts moving with an F 1 -score of 97.7%. The ResNet approach achieved an F 1- score of 100% at an average detection time of 0.144 s. The ResNet approach outperformed the SVM approach in both robustness against false positive detections and detection time.</abstract>
    <parentTitle language="eng">2018 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="doi">10.1109/IVS.2018.8500428</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Stefan Zernetsch</author>
    <author>Viktor Kreß</author>
    <author>Bernhard Sick</author>
    <author>Konrad Doll</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fußgänger</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
  <doc>
    <id>1490</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1200</pageFirst>
    <pageLast>1207</pageLast>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume>2019</volume>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Xiamen, China</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-02-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pose Based Trajectory Forecast of Vulnerable Road Users</title>
    <abstract language="eng">In this article, we investigate the use of 3D human poses for trajectory forecasting of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic. The forecast is based on past movements of the respective VRU and an important aspect in driver assistance systems and autonomous driving, which both could increase VRU safety. The 3D poses represent the entire body posture of the VRUs and can therefore provide important indicators for trajectory forecasting. In particular, we investigate the influence of different joint combinations and input sequence lengths of past movements on the accuracy of trajectory forecasts for pedestrians and cyclists. In addition, we divide VRU movements into the motion types wait, start, move, and stop and evaluate the results separately for each of them. Comparing it to an existing, solely head based trajectory forecast, we show the advantages of using 3D poses. With an input sequence length of 1.0 s, the forecasting error is reduced by 17.9 % for starting, 8.18 % for moving, and 11.0 % for stopping cyclists. For pedestrians, the error is reduced by 6.93 %, 2.73 %, and 5.02 %, respectively. With shorter input sequences, the improvements over the solely head based method remain for cyclists and even increase for pedestrians.</abstract>
    <parentTitle language="eng">IEEE Symposium Series on Computational Intelligence (SSCI)</parentTitle>
    <identifier type="doi">10.1109/SSCI44817.2019.9003023</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Viktor Kreß</author>
    <author>Stefan Zernetsch</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fußgänger</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
  <doc>
    <id>1173</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>518</pageFirst>
    <pageLast>523</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Bangalore, India</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-03-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Human Pose Estimation in Real Traffic Scenes</title>
    <parentTitle language="eng">2018 IEEE Symposium Series on Computational Intelligence (SSCI)</parentTitle>
    <identifier type="doi">10.1109/SSCI.2018.8628660</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Viktor Kreß</author>
    <author>Janis Jung</author>
    <author>Stefan Zernetsch</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fußgänger</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
</export-example>
