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Simon Genser¹, Stefan Muckenhuber^1,2, Selim Solmaz¹

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

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|November 27, 2021

Summary

This study introduces a new sensor modeling approach for advanced driver assistance systems (ADAS) and automated driving (AD). The method accurately simulates perception sensor errors, improving virtual testing and validation of ADAS/AD functions.

Keywords:

ADAS/AD function automotive camera automotive perception sensors sensor model virtual testing

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Area of Science:

Automotive Engineering
Perception Systems
Sensor Modeling

Background:

Virtual testing of advanced driver assistance systems (ADAS) and automated driving (AD) functions necessitates realistic perception sensor models.
Simulating limitations and measurement errors of real-world sensors is crucial for generating valid data for ADAS/AD system testing.

Purpose of the Study:

To introduce a novel sensor modeling approach for automotive perception sensors, focusing on position measurement errors.
To enable realistic simulation of sensor limitations for improved virtual testing of ADAS/AD.

Main Methods:

A novel approach combining kernel density estimation with regression modeling was developed.
The model is designed for automotive perception sensors providing object-level position estimations.
A Mobileye 630 camera was used for demonstration and evaluation, with data collected on a Hungarian highway.

Main Results:

The developed sensor model achieved a pointwise position error of 9.60% in the lateral and 1.57% in the longitudinal direction.
The model effectively captured the natural scattering and deviations in sensor measurement outputs.
The approach demonstrated successful modeling of position measurement errors for automotive perception sensors.

Conclusions:

The novel sensor modeling approach provides a realistic simulation of automotive perception sensor errors.
This method enhances the virtual testing and validation capabilities for advanced driver assistance and automated driving functions.
The approach is applicable to various automotive perception sensors providing object-level position data.