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.
Probabilistic hierarchical detection, representation and scene interpretation of lanes and roads
2012-07-01
968511 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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