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Reliably assessing the error in an estimated vehicle position is integral forensuring the vehicle's safety in urban environments. Many existing approachesuse GNSS measurements to characterize protection levels (PLs) as probabilisticupper bounds on the position error. However, GNSS signals might be reflected orblocked in urban environments, and thus additional sensor modalities need to beconsidered to determine PLs. In this paper, we propose a novel approach forcomputing PLs by matching camera image measurements to a LiDAR-based 3D map ofthe environment. We specify a Gaussian mixture model probability distributionof position error using deep neural network-based data-driven models andstatistical outlier weighting techniques. From the probability distribution, wecompute the PLs by evaluating the position error bound using numericalline-search methods. Through experimental validation with real-world data, wedemonstrate that the PLs computed from our method are reliable bounds on theposition error in urban environments.

Data-Driven Protection Levels for Camera and 3D Map-based Safe Urban Localization