Robust localization for autonomous vehicles in dense urban areas

Viana, Kerman; Díez, Mikel

doi:10.1109/icstcc52150.2021.9607194

Cited by 1 publication

(1 citation statement)

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“…In this work, a reconfigurable localization framework is proposed, suitable for dense urban areas and capable of detecting positioning errors and reconfiguring faulty modules accordingly to ensure correct and accurate localization. The proposed framework is an extension of the one presented by the authors in [ 37 ] and presents the following main contributions over the works previously analyzed: (1) a localization framework able to cope with different sensor failures thanks to the combination of fusion algorithms and decision strategies; (2) the definition within the framework of a sensor-measurement-based error-detection strategy, which allows determination of which sensor is failing so that the data provided by the failing sensor can be neglected; and (3) a novel reconfiguration module that evaluates the failure scenario and reconfigures the system, adopting alternative localization strategies that use remaining sensor data to avoid vehicle stop until the system is fully degraded.…”

Section: Introductionmentioning

confidence: 99%

A Reconfigurable Framework for Vehicle Localization in Urban Areas

Viana¹,

Díez²

2022

Sensors

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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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Section: Introductionmentioning

confidence: 99%