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

Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle's safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on the position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose a novel approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we evaluate its intervals using numerical line-search methods to compute the PL. We experimentally validate our approach using real-world data and demonstrate that the PLs computed from our method are reliable upper bounds on the position error in urban environments.