A Reconfigurable Framework for Vehicle Localization in Urban Areas
Accurate localization for autonomous vehicle operations is essential in dense urban areas. In order to ensure safety, positioning algorithms should implement fault detection and fallback strategies. While many strategies stop the vehicle once a failure is detected, in this work a new framework is pr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/7/2595 |
| _version_ | 1797437718390636544 |
|---|---|
| author | Kerman Viana Asier Zubizarreta Mikel Diez |
| author_facet | Kerman Viana Asier Zubizarreta Mikel Diez |
| author_sort | Kerman Viana |
| collection | DOAJ |
| description | Accurate localization for autonomous vehicle operations is essential in dense urban areas. In order to ensure safety, positioning algorithms should implement fault detection and fallback strategies. While many strategies stop the vehicle once a failure is detected, in this work a new framework is proposed that includes an improved reconfiguration module to evaluate the failure scenario and offer alternative positioning strategies, allowing continued driving in degraded mode until a critical failure is detected. Furthermore, as many failures in sensors can be temporary, such as GPS signal interruption, the proposed approach allows the return to a non-fault state while resetting the alternative algorithms used in the temporary failure scenario. The proposed localization framework is validated in a series of experiments carried out in a simulation environment. Results demonstrate proper localization for the driving task even in the presence of sensor failure, only stopping the vehicle when a fully degraded state is achieved. Moreover, reconfiguration strategies have proven to consistently reset the accumulated drift of the alternative positioning algorithms, improving the overall performance and bounding the mean error. |
| first_indexed | 2024-03-09T11:26:40Z |
| format | Article |
| id | doaj.art-7f03d45ee67e409a98de33fb69ae4312 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:26:40Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7f03d45ee67e409a98de33fb69ae43122023-12-01T00:01:38ZengMDPI AGSensors1424-82202022-03-01227259510.3390/s22072595A Reconfigurable Framework for Vehicle Localization in Urban AreasKerman Viana0Asier Zubizarreta1Mikel Diez2Faculty of Engineering in Bilbao, University of the Basque Country UPV/EHU, 48013 Bilbao, SpainFaculty of Engineering in Bilbao, University of the Basque Country UPV/EHU, 48013 Bilbao, SpainFaculty of Engineering in Bilbao, University of the Basque Country UPV/EHU, 48013 Bilbao, SpainAccurate localization for autonomous vehicle operations is essential in dense urban areas. In order to ensure safety, positioning algorithms should implement fault detection and fallback strategies. While many strategies stop the vehicle once a failure is detected, in this work a new framework is proposed that includes an improved reconfiguration module to evaluate the failure scenario and offer alternative positioning strategies, allowing continued driving in degraded mode until a critical failure is detected. Furthermore, as many failures in sensors can be temporary, such as GPS signal interruption, the proposed approach allows the return to a non-fault state while resetting the alternative algorithms used in the temporary failure scenario. The proposed localization framework is validated in a series of experiments carried out in a simulation environment. Results demonstrate proper localization for the driving task even in the presence of sensor failure, only stopping the vehicle when a fully degraded state is achieved. Moreover, reconfiguration strategies have proven to consistently reset the accumulated drift of the alternative positioning algorithms, improving the overall performance and bounding the mean error.https://www.mdpi.com/1424-8220/22/7/2595autonomous vehiclerobust localizationreconfigurationsensor fusion |
| spellingShingle | Kerman Viana Asier Zubizarreta Mikel Diez A Reconfigurable Framework for Vehicle Localization in Urban Areas Sensors autonomous vehicle robust localization reconfiguration sensor fusion |
| title | A Reconfigurable Framework for Vehicle Localization in Urban Areas |
| title_full | A Reconfigurable Framework for Vehicle Localization in Urban Areas |
| title_fullStr | A Reconfigurable Framework for Vehicle Localization in Urban Areas |
| title_full_unstemmed | A Reconfigurable Framework for Vehicle Localization in Urban Areas |
| title_short | A Reconfigurable Framework for Vehicle Localization in Urban Areas |
| title_sort | reconfigurable framework for vehicle localization in urban areas |
| topic | autonomous vehicle robust localization reconfiguration sensor fusion |
| url | https://www.mdpi.com/1424-8220/22/7/2595 |
| work_keys_str_mv | AT kermanviana areconfigurableframeworkforvehiclelocalizationinurbanareas AT asierzubizarreta areconfigurableframeworkforvehiclelocalizationinurbanareas AT mikeldiez areconfigurableframeworkforvehiclelocalizationinurbanareas AT kermanviana reconfigurableframeworkforvehiclelocalizationinurbanareas AT asierzubizarreta reconfigurableframeworkforvehiclelocalizationinurbanareas AT mikeldiez reconfigurableframeworkforvehiclelocalizationinurbanareas |