  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
compute the PLs by evaluating the position error bound using numerical
line-search methods. Through experimental validation with real-world data, we
demonstrate that the PLs computed from our method are reliable bounds on the
position error in urban environments.
