A Multi-Stage Clustering Framework for Automotive Radar Data

@article{Scheiner2019AMC,
  title={A Multi-Stage Clustering Framework for Automotive Radar Data},
  author={Nicolas Scheiner and N. Appenrodt and J. Dickmann and B. Sick},
  journal={2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
  year={2019},
  pages={2060-2067}
}
  • Nicolas Scheiner, N. Appenrodt, +1 author B. Sick
  • Published 2019
  • Computer Science, Engineering, Mathematics
  • 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
  • Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 15 REFERENCES
    A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
    • 13,927
    • PDF
    Grid-based DBSCAN for clustering extended objects in radar data
    • 31
    Semantic radar grids
    • 24
    Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
    • 5
    • PDF
    Radar-based Feature Design and Multiclass Classification for Road User Recognition
    • 7
    • PDF
    Supervised Clustering for Radar Applications: On the Way to Radar Instance Segmentation
    • 6
    Pedestrian Classification for 79 GHz Automotive Radar Systems
    • 9