Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via
  Automatic Label Generation
David Paz Narayanan E. Ranganatha Srinidhi K. Srinivas Yunchao Yao Henrik I. Christensen
This research work seeks to explore and identify strategies that can
determine road topology information in 2D and 3D under highly dynamic urban
driving scenarios. To facilitate this exploration, we introduce a substantial
dataset comprising nearly one million automatically labeled data frames. A key
contribution of our research lies in developing an automatic label-generation
process and an occlusion handling strategy. This strategy is designed to model
a wide range of occlusion scenarios, from mild disruptions to severe blockages.
Furthermore, we present a comprehensive ablation study wherein multiple
centerline detection methods are developed and evaluated. This analysis not
only benchmarks the performance of various approaches but also provides
valuable insights into the interpretability of these methods. Finally, we
demonstrate the practicality of our methods and assess their adaptability
across different sensor configurations, highlighting their versatility and
relevance in real-world scenarios. Our dataset and experimental models are
publicly available.