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Evaluating Localization Accuracy of Automated Driving Systems
- Source :
- Sensors (Basel, Switzerland), Sensors, Volume 21, Issue 17, Sensors, Vol 21, Iss 5855, p 5855 (2021)
- Publication Year :
- 2021
-
Abstract
- Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1&nbsp<br />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&nbsp<br />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.
- Subjects :
- Automobile Driving
Relation (database)
Computer science
TP1-1185
Biochemistry
GeneralLiterature_MISCELLANEOUS
Article
Analytical Chemistry
localization accuracy evaluation
Position (vector)
Data file
Electrical and Electronic Engineering
Closing (morphology)
Instrumentation
Ground truth
business.industry
Chemical technology
Accidents, Traffic
Pattern recognition
Atomic and Molecular Physics, and Optics
Path (graph theory)
automated driving
Trajectory
Artificial intelligence
ground-truth
business
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 17
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....989152ad15ab1c9b562e9cdb412c3efb