Many modern approaches for autonomous vehicles are still limited to a low-level data representation without considering complex relational information about their environment. This often leads to a generalization problem in complex situations where algorithms only perform well in tailored scenes. The general challenge lies in combining probabilistic information about partially observable traffic participants with a semantic description of the environment to reduce the complexity of a scene making it manageable. In this work, we present a novel approach for considering high-level information in traffic situations with respect to modeling a semantic representation of the vehicles' environment as well as uncertainties respecting their probabilities through relations between entities. The knowledge is described by first-order logic. Environmental information is stored in a multi-dimensional tensor which will be called Semantic Tensor. It allows to model complex scenes and functions as a very fast data structure. A novel similarity measure allows comparing any two Semantic Tensors to extract information about the spatiotemporal differences.
The Fingerprint of a Traffic Situation: A Semantic Relationship Tensor for Situation Description and Awareness
2018-11-01
865513 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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