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A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Magosi, Zoltan Ferenc ; Li, Hexuan ; Rosenberger, Philipp ; Wan, Li ; Eichberger, Arno (2022)
A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving.
In: Sensors, 2022, 22 (15)
doi: 10.26083/tuprints-00022032
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving
Language: English
Date: 22 August 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Sensors
Volume of the journal: 22
Issue Number: 15
Collation: 22 Seiten
DOI: 10.26083/tuprints-00022032
Corresponding Links:
Origin: Secondary publication DeepGreen

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.

Uncontrolled Keywords: radar sensor, machine perception, radar sensor model, automated driving, virtual testing
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-220320
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD)
Date Deposited: 22 Aug 2022 11:02
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22032
PPN: 498696332
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