WiFi-based Localization for Fail-Aware Autonomous Driving in Urban Scenarios.

Carlos Guindel Gómez, Adrián García Sánchez,Noelia Hernández Parra,Ignacio Parra Alonso,Euntai Kim

IV(2023)

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Abstract
Ego-localization is one of the most critical functions in autonomous vehicles. This paper presents a novel WiFi-based localization system for autonomous driving designed to augment onboard localization systems during critical failures or complement GNSS-denied scenarios such as parking lots. The system leverages the existing WiFi network infrastructure to provide global localization using a WiFi interface and a publicly available WiFi RSS and AP database created through survey efforts with conventional mobile devices. An LSTM-based architecture is trained to estimate the device's position from the history of WiFi RSS, leveraging temporal correlations in the sequences. The results suggest that this system is a viable alternative even when no strong requirements are set for the quality of the GNSS measurements in the surveying phase.
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Key words
autonomous vehicles,complement GNSS-denied scenarios,conventional mobile devices,critical failures,critical functions,ego-localization,existing WiFi network infrastructure,fail-aware autonomous driving,global localization,LSTM-based architecture,novel WiFi-based localization system,onboard localization systems,parking lots,publicly available WiFi RSS,urban scenarios,WiFi interface
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