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 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.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © Kerman Viana et al.
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Authors:
- Viana, Kerman
- Zubizarreta, Asier
- Diez, Mikel
- Publication Date: 2022
Language
- English
Media Info
- Media Type: Digital/other
- Pagination: 2595
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Serial:
- Sensors
- Volume: 22
- Issue Number: 7
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Algorithms; Autonomous vehicles; Data fusion; Sensors; Urban areas; Vehicle detectors
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01842542
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 15 2022 9:06AM