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We present a LiDAR-based and real-time capable 3D perception system forautomated driving in urban domains. The hierarchical system design is able tomodel stationary and movable parts of the environment simultaneously and underreal-time conditions. Our approach extends the state of the art by innovativein-detail enhancements for perceiving road users and drivable corridors even incase of non-flat ground surfaces and overhanging or protruding elements. Wedescribe a runtime-efficient pointcloud processing pipeline, consisting ofadaptive ground surface estimation, 3D clustering and motion classificationstages. Based on the pipeline's output, the stationary environment isrepresented in a multi-feature mapping and fusion approach. Movable elementsare represented in an object tracking system capable of using multiplereference points to account for viewpoint changes. We further enhance thetracking system by explicit consideration of occlusion and ambiguity cases. Oursystem is evaluated using a subset of the TUBS Road User Dataset. We enhancecommon performance metrics by considering application-driven aspects ofreal-world traffic scenarios. The perception system shows impressive resultsand is able to cope with the addressed scenarios while still preservingreal-time capability.

A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains