Radar-based Feature Design and Multiclass Classification for Road User Recognition

@article{Scheiner2018RadarbasedFD,
  title={Radar-based Feature Design and Multiclass Classification for Road User Recognition},
  author={Nicolas Scheiner and Nils Appenrodt and J{\"u}rgen Dickmann and Bernhard Sick},
  journal={2018 IEEE Intelligent Vehicles Symposium (IV)},
  year={2018},
  pages={779-786}
}
  • Nicolas Scheiner, Nils Appenrodt, +1 author Bernhard Sick
  • Published in
    IEEE Intelligent Vehicles…
    2018
  • Computer Science, Mathematics
  • The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. [...] Key Method From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large.Expand Abstract

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