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Lane-level scene annotations provide invaluable data in autonomous vehiclesfor trajectory planning in complex environments such as urban areas and cities.However, obtaining such data is time-consuming and expensive since laneannotations have to be annotated manually by humans and are as such hard toscale to large areas. In this work, we propose a novel approach for lanegeometry estimation from bird's-eye-view images. We formulate the problem oflane shape and lane connections estimation as a graph estimation problem wherelane anchor points are graph nodes and lane segments are graph edges. We traina graph estimation model on multimodal bird's-eye-view data processed from thepopular NuScenes dataset and its map expansion pack. We furthermore estimatethe direction of the lane connection for each lane segment with a separatemodel which results in a directed lane graph. We illustrate the performance ofour LaneGraphNet model on the challenging NuScenes dataset and provideextensive qualitative and quantitative evaluation. Our model shows promisingperformance for most evaluated urban scenes and can serve as a step towardsautomated generation of HD lane annotations for autonomous driving.

Lane Graph Estimation for Scene Understanding in Urban Driving