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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    Title :

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




    Publication date :

    2023-06-04


    Size :

    3012699 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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