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Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information

The Institute of Industrial Science (IIS), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
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ISPRS Int. J. Geo-Inf. 2019, 8(6), 288; https://doi.org/10.3390/ijgi8060288
Received: 27 May 2019 / Revised: 13 June 2019 / Accepted: 15 June 2019 / Published: 20 June 2019
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Abstract

Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm. View Full-Text
Keywords: intelligent transportation systems; autonomous vehicles; self-localization; OpenStreetMap; HD map intelligent transportation systems; autonomous vehicles; self-localization; OpenStreetMap; HD map
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    Doi: 10.5281/zenodo.3066820
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    Description: Datasets for 'Estmating autonomous vehicle localization error using 2D Geographic Information'
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Wong, K.; Javanmardi, E.; Javanmardi, M.; Kamijo, S. Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information. ISPRS Int. J. Geo-Inf. 2019, 8, 288.

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