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

Zoltan Ferenc Magosi, Hexuan Li, Philpp Rosenberger, Li Wan, Arno Eichberger

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Number of pages22
JournalSensors
Volume2022
Issue number22
DOIs
Publication statusPublished - 29 Jul 2022

Keywords

  • radar sensor
  • machine perception
  • radar sensor model
  • automated driving
  • virtual testing

ASJC Scopus subject areas

  • Automotive Engineering

Fields of Expertise

  • Mobility & Production

Treatment code (Nähere Zuordnung)

  • Review

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