Lane Marking Learning based on Crowdsourced Data In this paper, we propose a new algorithm that derives lane marking maps from crowdsourced data. We process the data in four steps: (i) We make use of a point landmark map and, if available, an existent lane marking map for trajectory optimization and alignment, (ii) use a custom DBSCAN variant to cluster observations that belong to the same lane marking, (iii) apply a novel graph fitting approach to extract lane marking dashes, lines and even complex structures such as splits and merges and (iv) optimize the graph geometry with domain knowledge. The process of pointlandmark and lane markingbased trajectory alignment and the lane marking derivation is repeated iteratively to improve the results. Evaluation is carried out on a 9km highway section by comparison with high accuracy aerial photographs and manually labeled ground truth lane markings.

Crowdsourced Semantic Edge Mapping for Autonomous Vehicles Highly accurate maps of the road infrastructure are a crucial cornerstone for selfdriving cars to enable navigation in complex traffic scenarios. Traditional methods for creating detailed maps of road environments involve expensive survey vehicles that cannot keep up with the frequent changes in the road network. In this paper, we propose a novel method to derive detailed highdefinition maps by crowd sourcing data using commodity sensors. Our system uses multisession featurebased visual SLAM to align submaps recorded by individual vehicles on a central backend server. We reconstruct 3D boundaries of road infrastructure elements such as road markings and road boundaries from semantic object contours detected in keyframes by a neural network. The result is a concise map of semantically meaningful objects suitable both for localization and higherlevel planning tasks of automated vehicles. We evaluate our method on realworld data against a globally referenced groundtruth map demonstrating a high level of detail and metric accuracy.