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High Definition (HD) maps are necessary for many applications of automateddriving (AD), but their manual creation and maintenance is very costly. Vehiclefleet data from series production vehicles can be used to automaticallygenerate HD maps, but the data is often incomplete and noisy. We propose asystem for the generation of HD maps from vehicle fleet data, which is tolerantto missing or misclassified detections and can handle drives with multipleroutes, generating a single complete map, model-free and without priorreference lines. Using randomly selected drives as pivot drives, a step-wiselateral sampling of detections is performed. These sampled points are thenclustered and aligned using Expectation Maximization (EM), estimating a lateraloffset for each drive to compensate localization errors. The clustered pointsare replaced with the maxima of their probability density function (PDF) andconnected to form polylines using a modified rectangular linear assignmentalgorithm. The data from vehicles on varying routes is then fused into ahierarchical singular map graph. The proposed approach achieves an averageaccuracy below 0.5 meters compared to a hand annotated ground truth map, aswell as correctly resolving lane splits and merges, proving the feasibility ofthe use of vehicle fleet data for the generation of highway HD maps.

HD Map Generation from Noisy Multi-Route Vehicle Fleet Data on Highways with Expectation Maximization