Radar Signal Processing Chain for Sensor Model Development

Holder, Martin; Slavik, Zora; D’hondt, Thomas

doi:10.1007/978-3-030-14628-3_11

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of these effects describes a modelable property of a sensor. An exemplary functional decomposition is presented for radar in [ 20 ], for lidar in [ 21 ], and for camera in [ 22 ]. The first sensor effect receives input information from the virtual vehicle environment (labeled

).…”

Section: Configurable Sensor Model Templatementioning

confidence: 99%

Configurable Sensor Model Architecture for the Development of Automated Driving Systems

Schmidt

Schlager

Muckenhuber

et al. 2021

Sensors

View full text Add to dashboard Cite

Sensor models provide the required environmental perception information for the development and testing of automated driving systems in virtual vehicle environments. In this article, a configurable sensor model architecture is introduced. Based on methods of model-based systems engineering (MBSE) and functional decomposition, this approach supports a flexible and continuous way to use sensor models in automotive development. Modeled sensor effects, representing single-sensor properties, are combined to an overall sensor behavior. This improves reusability and enables adaptation to specific requirements of the development. Finally, a first practical application of the configurable sensor model architecture is demonstrated, using two exemplary sensor effects: the geometric field of view (FoV) and the object-dependent FoV.

show abstract

).…”

Section: Configurable Sensor Model Templatementioning

confidence: 99%

Configurable Sensor Model Architecture for the Development of Automated Driving Systems

Schmidt

Schlager

Muckenhuber

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Functional decomposition is used to break down an overall sensor behavior into individually modelable effects and separate model development from parameterization effort [11]. A functional decomposition for perception sensors can be performed as shown by Holder et al on radar [12], by Rosenberger et al on lidar [13] and by Mohr et al on camera [14]. They introduce functional blocks and interfaces to model a sensor type specific signal processing chain.…”

Section: State Of the Artmentioning

confidence: 99%

Towards Serious Perception Sensor Simulation for Safety Validation of Automated Driving - A Collaborative Method to Specify Sensor Models

Linnhoff

Rosenberger

Schmidt

et al. 2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

Perception sensor modeling is essential for the safety validation of automated driving systems in virtual environments. Nevertheless, the community lacks a methodical approach to derive requirements for such sensor models that enables a serious application for safety validation in the first place. This article provides a method to specify sensor models for the environmental perception of automated driving systems.The key of the approach is a collaborative collection of causeeffect chains as the basis for specification. With this collection at hand, a tabular form is introduced to extract the relevance of the effect chains to be modeled. Combined profound expert assessments in the table enable the test engineer to specify sensor models within a traceable decision-making process.This work received funding from SET Level and VVM of the PEGASUS project family, promoted by the German Federal Ministry for Economic Affairs and Energy based on a decision of the Deutsche Bundestag.

show abstract

“…Here, perception sensor simulation has been identified as a major challenge of the overall simulation approach, as described in former work by the authors [5]. In this regard, functional decomposition is ideally suited to gain better insight into the information processing and loss during reception and processing by a perception sensor system, as it is highly sophisticated to reach valid perception sensor simulation [6,7,8]. It is a widely used method of choice for (safety) validation in general to reduce the exploding scenario space, when test automation is designed and scenarios are varied [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Highly Parameterizable and Generic Perception Sensor Model Architecture

Linnhoff¹,

Rosenberger²,

Holder³

et al. 2021

Proceedings

View full text Add to dashboard Cite

Scenario-based virtual testing is seen as a key element to bring the overall safety validation effort for automated driving functions to an economically feasible level. In this work, a generic and modular architecture for simulation of automotive perception sensors is introduced, as part of the overall virtual testing pipeline. It is based on the functional decomposition of real world perception sensors. All interfaces between the individual modules of the model architecture are oriented on internationally recognized standards and therefore facilitate a high degree of interchangeability. In addition, a wrapper framework handles all outer communication and enables a profile-based parameterization of the model, where every profile reflects a specific set of parameters tailored to the specifications and use case of the end user.

show abstract

Radar Signal Processing Chain for Sensor Model Development

Cited by 3 publications

References 8 publications

Configurable Sensor Model Architecture for the Development of Automated Driving Systems

Configurable Sensor Model Architecture for the Development of Automated Driving Systems

Towards Serious Perception Sensor Simulation for Safety Validation of Automated Driving - A Collaborative Method to Specify Sensor Models

Highly Parameterizable and Generic Perception Sensor Model Architecture

Contact Info

Product

Resources

About