RadarScenes

A pre-print of the RadarScenes paper can be found on ArXiv.org.

Further Publications

The RadarScenes data set was already used in a couple of journal- as well as conference papers. The following list of articles gives you some ideas, how the data set can be used.

Notice that the data set has grown over time and only a subset of the whole data set was used in the different articles.

  • O. Schumann, J. Lombacher, M. Hahn, C. Wöhler, J. Dickmann, “Scene Understanding with Automotive Radar”, in IEEE Transactions on Intelligent Vehicles, vol 5, no. 2, https://doi.org/10.1109/TIV.2019.2955853, 2020.
  • O. Schumann, M. Hahn, J. Dickmann, C. Wöhler, “Semantic Segmentation on Radar Point Clouds”, in 2018 21st International Conference on Information Fusion (FUSION), https://doi.org/10.23919/ICIF.2018.8455344, 2018.
  • O. Schumann, M. Hahn, J. Dickmann, C. Wöhler, “Supervised Clustering for Radar Applications: On the Way to Radar Instance Segmentation”, in 2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility, ICMIM 2018, https://doi.org/10.1109/ICMIM.2018.8443534, 2018.
  • O. Schumann, M. Hahn, J. Dickmann, C. Wöhler, “Comparison of random forest and long short-term memory network performances in classification tasks using radar”, in 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), https://doi.org/10.1109/SDF.2017.8126350, 2017.
  • J. F. Tilly, S. Haag, O. Schumann, F. Weishaupt, B. Duraisamy, J. Dickmann, M. Fritzsche, “Detection and Tracking on Automotive Radar Data with Deep Learning”, in 2020 IEEE 23rd International Conference on Information Fusion (FUSION), https://doi.org/10.23919/FUSION45008.2020.9190261, 2020.
  • N. Scheiner, O. Schumann, F. Kraus, N. Appenrodt, J. Dickmann, B. Sick, “Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?”, in 2020 IEEE 23rd International Conference on Information Fusion (FUSION), https://doi.org/10.23919/FUSION45008.2020.9190338, 2020.
  • N. Scheiner, N. Appenrodt, J. Dickmann, B. Sick, “Radar-based Feature Design and Multiclass Classification for Road User Recognition”, in 2018 IEEE Intelligent Vehicles Symposium (IV), https://doi.org/10.1109/IVS.2018.8500607, 2018.
  • N. Scheiner, N. Appenrodt, J. Dickmann, B. Sick, “A multi-stage clustering framework for automotive radar data”, in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), https://doi.org/10.1109/ITSC.2019.8916873, 2019.
  • S. Haag, B. Duraisamy, W. Koch and J. Dickmann, “Classification Assisted Tracking for Autonomous Driving Domain,” in 2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF), https://doi.org/10.1109/SDF.2018.8547138, 2018.
  • S. Haag, B. Duraisamy, W. Koch and J. Dickmann, “Radar and Lidar Target Signatures of Various Object Types and Evaluation of Extended Object Tracking Methods for Autonomous Driving Applications,” 2018 21st International Conference on Information Fusion (FUSION), https://doi.org/10.23919/ICIF.2018.8455395, 2018.
  • S. Haag, B. Duraisamy, F. Govaers, W. Koch, M. Fritzsche and J. Dickmann, “Extended Object Tracking assisted Adaptive Clustering for Radar in Autonomous Driving Applications,” 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), https://doi.org/10.1109/SDF.2019.8916658, 2019.
  • S. Haag, B. Duraisamy, F. Govaers, W. Koch, M. Fritzsche and J. Dickmann, “BAAS: Bayesian Tracking and Fusion Assisted Object Annotation of Radar Sensor Data for Artificial Intelligence Application,” 2020 IEEE Radar Conference (RadarConf20) https://doi.org/10.1109/RadarConf2043947.2020.9266698, 2020.