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Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding

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Fahrerassistenzsysteme 2018

Part of the book series: Proceedings ((PROCEE))

Abstract

The challenges for sensors and their correlated perception algorithms for driverless vehicles are tremendous. They have to provide more comprehensively than ever before a model of the complete static and dynamic surroundings of the ego-vehicle to understand the correlation of both with reference to the ego-vehicle’s movement. For dynamic objects, this means that radar has to provide the dimension and complete motion state as well as the class information, in highway, rural, and inner city scenarios. For the static world, new algorithm schemes have to be developed to enhance the shape representation of an object by image like semantics. In order to generate the necessary information, radar networking for 360° coverage have to be reinvented. Radar data processing toolchains have to be revolutionized by applying artificial intelligence and advanced signal processing in a synergetic manner.

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Author information

Authors and Affiliations

  1. Daimler AG, Sindelfingen, Germany

    Jürgen Dickmann, Jakob Lombacher, Ole Schumann, Nicolas Scheiner, Saeid K. Dehkordi, Tilmann Giese & Bharanidhar Duraisamy

Authors
  1. Jürgen Dickmann
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  2. Jakob Lombacher
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  3. Ole Schumann
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  4. Nicolas Scheiner
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  5. Saeid K. Dehkordi
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  6. Tilmann Giese
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  7. Bharanidhar Duraisamy
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Corresponding author

Correspondence to Jürgen Dickmann .

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Editors and Affiliations

  1. Technische Universität Dortmund, Dortmund, Germany

    Torsten Bertram

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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Dickmann, J. et al. (2019). Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding. In: Bertram, T. (eds) Fahrerassistenzsysteme 2018. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-23751-6_1

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