New Challenges for Deep Neural Networks in Automotive Radar Perception

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New Challenges for Deep Neural Networks in Automotive Radar Perception

An Overview of Current Research Trends

Scheiner, Nicolas / Weishaupt, Fabio / Tilly, Julius F. / Dickmann, Jurgen

Radar sensors are a key component of automated vehicles. The requirements for radar perception modules are growing more demanding. At the same time, the radar sensors themselves are becoming increasingly sophisticated. Both developments lead to the progression of very complex algorithms. In the field of machine learning, increased task difficulty is often managed by using various types of deep neural networks (DNN). Deeper and more complex network structures allow for achieving results that had, until recently, been considered unattainable. In order to make use of this new set of machine learning algorithms, particular attention must be paid to the quality of the input data. This article gives an overview of some of the most promising ideas that will define the near to mid-term future in the field of DNNs in automotive radar perception. Contrary to image- or lidar-based approaches, the main challenge towards using DNNs on automotive radar data is information sparsity at a perception level. This currently prevents riding on the wave of recent successes in the object detection area. Another difficulty is the need for large amounts of labeled data. For information sparsity, important solutions such as high resolution processing or the utilization of low-level data layers and polarimetric radars are discussed. Furthermore, the annotation problem is derived using an example and a practical solution for the realization of an auto-labeling system is described.

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Dokumentinformationen

Titel :

New Challenges for Deep Neural Networks in Automotive Radar Perception

Untertitel :

An Overview of Current Research Trends

Weitere Titelangaben:

Proceedings

Beteiligte:

Bertram, Torsten (Herausgeber:in) / Scheiner, Nicolas (Autor:in) / Weishaupt, Fabio (Autor:in) / Tilly, Julius F. (Autor:in) / Dickmann, Jurgen (Autor:in)

Erschienen in:

Automatisiertes Fahren 2020 ; Kapitel : 14 ; 165-182

Proceedings

Erscheinungsdatum :

2021-08-03

Format / Umfang :

18 pages

ISBN :

978-3-658-34752-9 , 978-3-658-34751-2

ISSN :

2198-7440 , 2198-7432

DOI :