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

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.