  High Definition (HD) maps are necessary for many applications of automated
driving (AD), but their manual creation and maintenance is very costly. Vehicle
fleet data from series production vehicles can be used to automatically
generate HD maps, but the data is often incomplete and noisy. We propose a
system for the generation of HD maps from vehicle fleet data, which is tolerant
to missing or misclassified detections and can handle drives with multiple
routes, generating a single complete map, model-free and without prior
reference lines. Using randomly selected drives as pivot drives, a step-wise
lateral sampling of detections is performed. These sampled points are then
clustered and aligned using Expectation Maximization (EM), estimating a lateral
offset for each drive to compensate localization errors. The clustered points
are replaced with the maxima of their probability density function (PDF) and
connected to form polylines using a modified rectangular linear assignment
algorithm. The data from vehicles on varying routes is then fused into a
hierarchical singular map graph. The proposed approach achieves an average
accuracy below 0.5 meters compared to a hand annotated ground truth map, as
well as correctly resolving lane splits and merges, proving the feasibility of
the use of vehicle fleet data for the generation of highway HD maps.
