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 point-landmark- and lane marking-based 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.

Authors

• David Pannen
• Martin Liebner
• Wolfram Burgard

Keywords

• Systematics
• Roads
• Fitting
• Clustering algorithms
• Robot sensing systems
• Data mining
• Noise measurement
• Iterative methods
• Trajectory optimization
• Standards
• Crowdsourced Data
• Lane Markings
• Complex Structure
• Aerial Images
• Central Point
• Distance Function
• Point Cloud
• Longitudinal Direction
• Graph Structure
• Lateral Direction
• Optimization Step
• Global Navigation Satellite System
• Problematic Features
• Local Coordinate System
• Distance Vector
• Density-based Clustering
• Automated Vehicles
• Simultaneous Localization And Mapping
• Subsequent Optimization
• Polyline
• Mean Orientation
• Factor Graph
• Graph Optimization
• Global Accuracy
• Cluster Membership
• 3D Space
• Single Cluster
• Clustering Algorithm