A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking
Integrating Automated Vehicles (AVs) into everyday traffic is an ongoing challenge. Ensuring the safety of all involved agents, even in the presence of system failures, is crucial, especially in urban environments. This paper introduces a fallback-oriented localization algorithm for AVs designed to...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10963758/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849335833899827200 |
|---|---|
| author | Mario Rodriguez-Arozamena Jose Matute Javier Araluce Lukas Kuschnig Christoph Pilz Markus Schratter Joshue Perez Rastelli Asier Zubizarreta |
| author_facet | Mario Rodriguez-Arozamena Jose Matute Javier Araluce Lukas Kuschnig Christoph Pilz Markus Schratter Joshue Perez Rastelli Asier Zubizarreta |
| author_sort | Mario Rodriguez-Arozamena |
| collection | DOAJ |
| description | Integrating Automated Vehicles (AVs) into everyday traffic is an ongoing challenge. Ensuring the safety of all involved agents, even in the presence of system failures, is crucial, especially in urban environments. This paper introduces a fallback-oriented localization algorithm for AVs designed to operate during main localization source failures. The method leverages stationary vehicles as dynamic landmarks, identified through the perception module, despite their initially unknown positions. By tracking relative positions before failure and applying trilateration, the algorithm estimates the ego vehicle's position. The proposed algorithm is evaluated through simulations, a real-world dataset, and practical tests on two vehicle models. The results include an average trajectory error of 0.62 m and 1.58 deg compared to the ground truth over different fallback maneuvers. This translates into an average relative translational error of 1.65% and a relative rotational error of 0.05 deg/m, improving the performance of an IMU-based dead reckoning and, hence, providing localization for performing safe stop maneuvers. |
| format | Article |
| id | doaj-art-46a3ea3811e44ddda48a217be86ae27b |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-46a3ea3811e44ddda48a217be86ae27b2025-08-20T03:45:10ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0161085109910.1109/OJVT.2025.356019810963758A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and TrackingMario Rodriguez-Arozamena0https://orcid.org/0000-0001-5294-0406Jose Matute1https://orcid.org/0000-0003-2754-7623Javier Araluce2https://orcid.org/0000-0001-5101-0485Lukas Kuschnig3https://orcid.org/0000-0001-8761-2576Christoph Pilz4https://orcid.org/0000-0002-6937-4065Markus Schratter5https://orcid.org/0000-0001-6054-9669Joshue Perez Rastelli6https://orcid.org/0000-0002-0974-5303Asier Zubizarreta7https://orcid.org/0000-0001-6049-2308TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, SpainVirginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA, USATECNALIA, Basque Research and Technology Alliance (BRTA), Derio, SpainDepartment of Electrics, Electronics, and Software, Virtual Vehicle Research GmbH, Graz, AustriaDepartment of Electrics, Electronics, and Software, Virtual Vehicle Research GmbH, Graz, AustriaDepartment of Electrics, Electronics, and Software, Virtual Vehicle Research GmbH, Graz, AustriaCEIT-Basque Research and Technology Alliance, Donostia-San Sebastián, San Sebastián, SpainDepartment of Automatic Control and Systems Engineering, University of the Basque Country UPV/EHU, Bilbao, SpainIntegrating Automated Vehicles (AVs) into everyday traffic is an ongoing challenge. Ensuring the safety of all involved agents, even in the presence of system failures, is crucial, especially in urban environments. This paper introduces a fallback-oriented localization algorithm for AVs designed to operate during main localization source failures. The method leverages stationary vehicles as dynamic landmarks, identified through the perception module, despite their initially unknown positions. By tracking relative positions before failure and applying trilateration, the algorithm estimates the ego vehicle's position. The proposed algorithm is evaluated through simulations, a real-world dataset, and practical tests on two vehicle models. The results include an average trajectory error of 0.62 m and 1.58 deg compared to the ground truth over different fallback maneuvers. This translates into an average relative translational error of 1.65% and a relative rotational error of 0.05 deg/m, improving the performance of an IMU-based dead reckoning and, hence, providing localization for performing safe stop maneuvers.https://ieeexplore.ieee.org/document/10963758/Automated vehiclesfallbacklandmark localizationtrilateration |
| spellingShingle | Mario Rodriguez-Arozamena Jose Matute Javier Araluce Lukas Kuschnig Christoph Pilz Markus Schratter Joshue Perez Rastelli Asier Zubizarreta A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking IEEE Open Journal of Vehicular Technology Automated vehicles fallback landmark localization trilateration |
| title | A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking |
| title_full | A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking |
| title_fullStr | A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking |
| title_full_unstemmed | A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking |
| title_short | A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking |
| title_sort | fallback localization algorithm for automated vehicles based on object detection and tracking |
| topic | Automated vehicles fallback landmark localization trilateration |
| url | https://ieeexplore.ieee.org/document/10963758/ |
| work_keys_str_mv | AT mariorodriguezarozamena afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT josematute afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT javieraraluce afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT lukaskuschnig afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT christophpilz afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT markusschratter afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT joshueperezrastelli afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT asierzubizarreta afallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT mariorodriguezarozamena fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT josematute fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT javieraraluce fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT lukaskuschnig fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT christophpilz fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT markusschratter fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT joshueperezrastelli fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking AT asierzubizarreta fallbacklocalizationalgorithmforautomatedvehiclesbasedonobjectdetectionandtracking |