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Evaluating Localization Accuracy of Automated Driving Systems
Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433815/ https://www.ncbi.nlm.nih.gov/pubmed/34502746 http://dx.doi.org/10.3390/s21175855 |
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author | Rehrl, Karl Gröchenig, Simon |
author_facet | Rehrl, Karl Gröchenig, Simon |
author_sort | Rehrl, Karl |
collection | PubMed |
description | Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve. |
format | Online Article Text |
id | pubmed-8433815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84338152021-09-12 Evaluating Localization Accuracy of Automated Driving Systems Rehrl, Karl Gröchenig, Simon Sensors (Basel) Article Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve. MDPI 2021-08-30 /pmc/articles/PMC8433815/ /pubmed/34502746 http://dx.doi.org/10.3390/s21175855 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rehrl, Karl Gröchenig, Simon Evaluating Localization Accuracy of Automated Driving Systems |
title | Evaluating Localization Accuracy of Automated Driving Systems |
title_full | Evaluating Localization Accuracy of Automated Driving Systems |
title_fullStr | Evaluating Localization Accuracy of Automated Driving Systems |
title_full_unstemmed | Evaluating Localization Accuracy of Automated Driving Systems |
title_short | Evaluating Localization Accuracy of Automated Driving Systems |
title_sort | evaluating localization accuracy of automated driving systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433815/ https://www.ncbi.nlm.nih.gov/pubmed/34502746 http://dx.doi.org/10.3390/s21175855 |
work_keys_str_mv | AT rehrlkarl evaluatinglocalizationaccuracyofautomateddrivingsystems AT grochenigsimon evaluatinglocalizationaccuracyofautomateddrivingsystems |