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首页> 外文会议>2012 IEEE International Conference on Vehicular Electronics and Safety >Probabilistic hierarchical detection, representation and scene interpretation of lanes and roads
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Probabilistic hierarchical detection, representation and scene interpretation of lanes and roads

机译：车道和道路的概率层次检测，表示和场景解释

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摘要

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.
机译：本文的重点是提出一种对车道和道路及其在车辆周围环境中可能扮演的角色进行综合检测，表示和解释的概念。这包括针对多个抽象级别使用多个概率密度函数的粒子逼近的分层概率表示。可以集成低级和高级信息，从而导致场景表示的自下而上和自上而下的细化。基于该表示，对贝叶斯网络进行建模以概率性地推断抽象的，而不是直接可观察的关系。基于这些关系，将生成所有假设的一致子集来表示当前情况。该方法具有高度的灵活性，能够集成具有不同抽象级别的不同信息源，同时保留较高级别的概率详细信息。

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