Efficient Road Scene Understanding for Intelligent Vehicles Using Compositional Hierarchical Models

In this paper, we present a novel compositional hierarchical framework for road scene understanding that allows for reliable estimation of scene topologies, such as the number, location, and width of lanes and the lane topology, i.e., parallel, splitting, or merging. In our approach, lanes and roads are represented in a hierarchical compositional model in which nodes represent parts of roads and edges represent probabilistic constraints between pairs of parts. A key benefit of our approach is the representation of lanes and roads as a set of common parts. This makes our approach applicable to scenes with rich topological diversity, while bringing along the much desired computational efficiency. To cope with the high-dimensional and continuous parameter space of our model and the non-Gaussian image evidence, we perform inference using nonparametric belief propagation. Based on this approximate inference algorithm, we introduce depth-first message passing for lane detection, which performs inference in several sweeps. Empirical results show that depth-first message passing requires significantly lower computation for performance comparable with classical belief propagation.