  We present a probabilistic ego-lane estimation algorithm for highway-like
scenarios that is designed to increase the accuracy of the ego-lane estimate,
which can be obtained relying only on a noisy line detector and tracker. The
contribution relies on a Hidden Markov Model (HMM) with a transient failure
model. The proposed algorithm exploits the OpenStreetMap (or other cartographic
services) road property lane number as the expected number of lanes and
leverages consecutive, possibly incomplete, observations. The algorithm
effectiveness is proven by employing different line detectors and showing we
could achieve much more usable, i.e. stable and reliable, ego-lane estimates
over more than 100 Km of highway scenarios, recorded both in Italy and Spain.
Moreover, as we could not find a suitable dataset for a quantitative comparison
with other approaches, we collected datasets and manually annotated the Ground
Truth about the vehicle ego-lane. Such datasets are made publicly available for
usage from the scientific community.
