Comparing a Linear Filter with a ML-based Approach for Modeling Perception Errors of Automated Vehicles

Martin Sigl; Andreas Lebherz; Christoph Schutz; Sebastian Wagner; Daniel Watzenig

doi:10.1109/ICCVE52871.2022.9742719

Comparing a Linear Filter with a ML-based Approach for Modeling Perception Errors of Automated Vehicles

Martin Sigl, Andreas Lebherz, Christoph Schutz, Sebastian Wagner, Daniel Watzenig

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

Virtual tests are used increasingly to assess automated driving. They are generally based on ground truth information. The perception process of automated vehicles with sensors provides a shifted, erroneous view of the environment. Within the virtual assessment process of automated driving, it is necessary to restore this sensor view for the automated driving function to ensure realistic testing. This paper presents two approaches to derive models that are able to recreate these perception errors. Both approaches are evaluated regarding their overall performance to emulate the sensor view of automated vehicles. They are able to create models that significantly reduce the difference between ground truth and an automated vehicle's sensor view of the environment. The resulting models can be used to improve the virtual assessment process.

Originalsprache	englisch
Titel	ICCVE 2022 - IEEE International Conference on Connected Vehicles and Expo
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers
ISBN (elektronisch)	9781665416870
DOIs	https://doi.org/10.1109/ICCVE52871.2022.9742719
Publikationsstatus	Veröffentlicht - 2022
Veranstaltung	2022 International Conference on Connected Vehicles & Expo: ICCVE 2022 - Florida Polytechnic University, Hybrider Event, USA / Vereinigte Staaten Dauer: 7 März 2022 → 9 März 2022 https://iccve2022.org/

Konferenz

Konferenz	2022 International Conference on Connected Vehicles & Expo
Kurztitel	ICCVE 2022
Land/Gebiet	USA / Vereinigte Staaten
Ort	Hybrider Event
Zeitraum	7/03/22 → 9/03/22
Internetadresse	https://iccve2022.org/

ASJC Scopus subject areas

Angewandte Informatik
Fahrzeugbau
Sicherheit, Risiko, Zuverlässigkeit und Qualität
Steuerung und Optimierung

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10.1109/ICCVE52871.2022.9742719

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Comparing a Linear Filter with a ML-based Approach for Modeling Perception Errors of Automated Vehicles. / Sigl, Martin; Lebherz, Andreas; Schutz, Christoph et al.
ICCVE 2022 - IEEE International Conference on Connected Vehicles and Expo. Institute of Electrical and Electronics Engineers, 2022.

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Sigl, M, Lebherz, A, Schutz, C, Wagner, S & Watzenig, D 2022, Comparing a Linear Filter with a ML-based Approach for Modeling Perception Errors of Automated Vehicles. in ICCVE 2022 - IEEE International Conference on Connected Vehicles and Expo. Institute of Electrical and Electronics Engineers, 2022 International Conference on Connected Vehicles & Expo, Hybrider Event, Florida, USA / Vereinigte Staaten, 7/03/22. https://doi.org/10.1109/ICCVE52871.2022.9742719

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abstract = "Virtual tests are used increasingly to assess automated driving. They are generally based on ground truth information. The perception process of automated vehicles with sensors provides a shifted, erroneous view of the environment. Within the virtual assessment process of automated driving, it is necessary to restore this sensor view for the automated driving function to ensure realistic testing. This paper presents two approaches to derive models that are able to recreate these perception errors. Both approaches are evaluated regarding their overall performance to emulate the sensor view of automated vehicles. They are able to create models that significantly reduce the difference between ground truth and an automated vehicle's sensor view of the environment. The resulting models can be used to improve the virtual assessment process.",

keywords = "Autonomous Vehicles, Deep Learning, linear time invariant element, LSTM, Sensor Errors, Sensor Models, Sensor Systems, Simulation, Vehicle Detection",

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doi = "10.1109/ICCVE52871.2022.9742719",

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AU - Lebherz, Andreas

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AU - Wagner, Sebastian

AU - Watzenig, Daniel

PY - 2022

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AB - Virtual tests are used increasingly to assess automated driving. They are generally based on ground truth information. The perception process of automated vehicles with sensors provides a shifted, erroneous view of the environment. Within the virtual assessment process of automated driving, it is necessary to restore this sensor view for the automated driving function to ensure realistic testing. This paper presents two approaches to derive models that are able to recreate these perception errors. Both approaches are evaluated regarding their overall performance to emulate the sensor view of automated vehicles. They are able to create models that significantly reduce the difference between ground truth and an automated vehicle's sensor view of the environment. The resulting models can be used to improve the virtual assessment process.

KW - Autonomous Vehicles

KW - Deep Learning

KW - linear time invariant element

KW - LSTM

KW - Sensor Errors

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KW - Simulation

KW - Vehicle Detection

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BT - ICCVE 2022 - IEEE International Conference on Connected Vehicles and Expo

PB - Institute of Electrical and Electronics Engineers

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Comparing a Linear Filter with a ML-based Approach for Modeling Perception Errors of Automated Vehicles

Abstract

Konferenz

ASJC Scopus subject areas

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