Probabilistic hierarchical detection, representation and scene interpretation of lanes and roads

The focus of this paper is to propose a concept for integrated detection, representation and interpretation of lanes and roads as well as their possible roles in the vehicle´s surrounding. This includes a hierarchical probabilistic representation using particle approximation of multiple probability density functions for different levels of abstraction. Low- and high-level information can be integrated, leading to mutual bottom-up and top-down refinement of scene representation. Based on this representation bayesian networks are modeled for probabilistically inferring abstract, not directly observable relations. Based on these relations, a consistent subset of all hypotheses is generated to represent the current situation. The approach is highly flexible, able to integrate different information sources of varying levels of abstraction, while preserving a high level of probabilistic detail.