<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <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>570</id>
    <completedYear>2014</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1758</pageFirst>
    <pageLast>1763</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Qingdao, China</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2014-10-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis on Termination of Pedestrians‘ Gait at Urban Intersections</title>
    <parentTitle language="eng">Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on</parentTitle>
    <identifier type="doi">10.1109/ITSC.2014.6957947</identifier>
    <enrichment key="copyright">0</enrichment>
    <author>Michael Goldhammer</author>
    <author>Andreas Hubert</author>
    <author>Sebastian Köhler</author>
    <author>Klaus Zindler</author>
    <author>Ulrich Brunsmann</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>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Robotics and Automation</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
  <doc>
    <id>623</id>
    <completedYear>2014</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4110</pageFirst>
    <pageLast>4115</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2015-06-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks</title>
    <parentTitle language="eng">22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden</parentTitle>
    <identifier type="isbn">978-1-4799-5208-3</identifier>
    <identifier type="issn">1051-4651</identifier>
    <identifier type="doi">10.1109/ICPR.2014.704</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Michael Goldhammer</author>
    <author>Konrad Doll</author>
    <author>Ulrich Brunsmann</author>
    <author>Andre Gensler</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>
    <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>1420</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2381</pageFirst>
    <pageLast>2386</pageLast>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Auckland</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-12-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pose Based Start Intention Detection of Cyclists</title>
    <abstract language="eng">In this work, we present a new approach for start intention detection of cyclists based on 3D human pose estimation to increase their safety in road traffic. Start intention detection is realized using sequences of frame-wise estimated 3D poses. The poses were obtained by image sequences recorded by a stereo camera mounted behind the windshield of a moving vehicle. For training and evaluation, a dataset with 206 starting cyclists was created in real traffic. We demonstrate the advantages of this approach by comparing it to an existing, solely head trajectory based method. In particular, we investigate the performance for different observed time horizons ranging from 0.12 s up to 1.0 s as inputs for the two methods. This is of special importance for the protection of cyclists in road traffic, as they often only become visible to approaching vehicles shortly before dangerous situations occur. With an input length of 1.0 s the solely head trajectory based approach detects starting motions on average 0.834 s after the first motion of the bicycle with an F1-score of 97.5 %. The pose based approach outperforms these results by achieving the same F1-score 0.135 s earlier. The advantages of the pose based method become even more obvious with shorter input lengths. With an input length of 0.12 s, the head based approach achieves an F1-score of 93.5 % after 2.37 s, while the same score is reached after 0.668 s using poses.</abstract>
    <identifier type="doi">10.1109/ITSC.2019.8917215</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</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>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrrad</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>1395</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>219</pageFirst>
    <pageLast>228</pageLast>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Gesellschaft für Informatik e.V.</publisherName>
    <publisherPlace>Bonn</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-11-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Start Intention Detection of Cyclists using an LSTM Network</title>
    <abstract language="eng">In this article, we present an approach for start intention detection of cyclists based on their head trajectories. Therefore, we are using a network architecture based on Long Short-Term Memory (LSTM) cells, which is able to handle input sequences of different lengths. This is important because, for example, due to occlusions, cyclists often only become visible to approaching vehicles shortly before dangerous situations occur. Hence, the dependency of the results on the input sequence length is investigated. We use a dataset with 206 situations where cyclists were transitioning from waiting to moving that was recorded from a moving vehicle in inner-city traffic.With an input sequence length of 1.0 s we achieve an F1-score of 96.2% on average 0.680 s after the first movement of the bicycle. We obtain similar results for sequence lengths down to 0.2 s. For shorter sequences, the results regarding the F1-score and the mean detection time deteriorate considerably.</abstract>
    <parentTitle language="eng">INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)</parentTitle>
    <identifier type="isbn">978-3-88579-689-3</identifier>
    <identifier type="doi">10.18420/inf2019_ws25</identifier>
    <identifier type="issn">1617-5468</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Viktor Kreß</author>
    <editor>Claude Draude</editor>
    <author>Janis Jung</author>
    <editor>Martin Lange</editor>
    <author>Stefan Zernetsch</author>
    <editor>Bernhard Sick</editor>
    <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>Fahrrad</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>1497</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Maui, HI, USA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-12-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure</title>
    <abstract language="eng">In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange. This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system. Two tracking models are presented and compared. The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device. Our aim is to overcome limitations of tracking approaches based on single data sources. We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system. We, therefore, contribute to the safety of vulnerable road users in future traffic.</abstract>
    <parentTitle language="deu">21st International Conference on Intelligent Transportation Systems (ITSC) 2018</parentTitle>
    <identifier type="doi">10.1109/ITSC.2018.8569267</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Günther Reitberger</author>
    <author>Maarten Bieshaar</author>
    <author>Stefan Zernetsch</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <author>Erich Fuchs</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrrad</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Sensortechnik</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>
  <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>1501</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Paris, France</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-08-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks</title>
    <abstract language="eng">In this article, we present an approach to forecast trajectories of vulnerable road users (VRUs) including a numerical quantification of the uncertainty of the forecast. The uncertainty estimates are modeled as normal distributions by means of neural networks. Additionally, we present a method to evaluate the reliability of the forecasted uncertainty estimates, where we utilize quantile-quantile (Q-Q) plots, a graphical method to compare two distributions widely used in statistics. The positional accuracy is evaluated using Euclidean distances, in specific we use the average Euclidean error (AEE) and the average specific AEE (ASAEE). The model is trained and tested using a large dataset of 1311 cyclist trajectories, recorded at an urban intersection in real world traffic. Using this method, we achieve a similar positional accuracy compared to our previous work, where only positions are forecasted. The method is able to produce reliable uncertainty estimates for the motion types start, stop, turn left, and turn rightand produces underconfident uncertainty estimates for the motion types waitand move straight. Since uncertainties are not underestimated, the method can be used as a basis for trajectory planing in automated vehicles.</abstract>
    <parentTitle language="eng">2019 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="doi">10.1109/IVS.2019.8814258</identifier>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Stefan Zernetsch</author>
    <author>Hannes Reichert</author>
    <author>Viktor Kreß</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>Fahrrad</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>1499</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>534</pageFirst>
    <pageLast>544</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>3</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-10-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble</title>
    <abstract language="eng">In the future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this paper, we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing feature- and decision-level cooperation. We introduce a novel method based on a three-dimensional convolutional neural network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.</abstract>
    <parentTitle language="eng">IEEE Transactions on Intelligent Vehicles</parentTitle>
    <enrichment key="copyright">1</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Maarten Bieshaar</author>
    <author>Stefan Zernetsch</author>
    <author>Andreas Hubert</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>Fahrrad</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Sensortechnik</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>1697</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>Honolulu, HI, USA</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <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>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>
</export-example>
