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Bayesian Gaussian Mixture Models for Enhanced Radar Sensor Modeling: A Data-Driven Approach towards Sensor Simulation

Kelvin Walenta^1,2, Simon Genser¹, Selim Solmaz¹

¹Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This study presents a data-driven radar sensor model using Gaussian mixture models to improve Advanced Driver Assistance and Automated Driving (ADAS/AD) system testing. The model accurately predicts radar perception, enhancing road safety simulations.

Keywords:

ADAS/AD development Gaussian mixture models data-driven modeling radar modeling sensor simulation virtual validation

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Area of Science:

Robotics and Autonomous Systems
Sensor Modeling
Road Safety Engineering

Background:

Advanced Driver Assistance and Automated Driving (ADAS/AD) systems are crucial for road safety and vehicle automation.
Comprehensive testing of ADAS/AD systems, particularly in virtual environments, is essential due to increasing system complexity.
Radar sensors are vital components of ADAS/AD, but accurately modeling their perception, specifically radar cross-section (RCS), is challenging.

Purpose of the Study:

To develop a data-driven radar sensor model for virtual testing of ADAS/AD systems.
To accurately represent radar perception, including radar cross-section (RCS) and scatter point distribution.
To create a flexible and extensible framework for radar sensor modeling.

Main Methods:

Utilized Gaussian mixture models (GMMs) for data-driven modeling of radar perception across different vehicles and aspect angles.
Employed a Bayesian variational approach for automatic inference of model complexity.
Expanded the GMM into a comprehensive radar sensor model incorporating object lists, occlusion effects, and RCS-based detectability.

Main Results:

The developed model accurately reproduced radar cross-section (RCS) behavior and scatter point distributions.
Demonstrated the model's effectiveness in various simulated driving scenarios.
Validated the flexible and modular framework's capability for modeling specific radar aspects and extensibility.

Conclusions:

The data-driven radar sensor model effectively enhances the virtual testing of ADAS/AD systems.
The proposed framework offers a robust solution for modeling radar perception, contributing to improved road safety.
Further validation is recommended to refine model accuracy and expand its applicability in diverse scenarios.