By Michael Huelsen
The comprehension of a site visitors scenario performs an important function in using a motor vehicle. Interpretable details kinds a foundation for destiny projection, choice making and motion appearing, akin to navigating, maneuvering and riding keep an eye on. Michael Huelsen presents an ontology-based typical site visitors state of affairs description able to providing a variety of complex driving force counsel structures with correct information regarding the present site visitors scenario of a automobile and its atmosphere. those platforms are enabled to accomplish average activities and process visionary targets equivalent to harm and twist of fate loose riding, enormous information in arbitrary events as much as even self sufficient driving.
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Extra info for Knowledge-Based Driver Assistance Systems: Traffic Situation Description and Situation Feature Relevance
2011, Schamm and Zöllner, 2011]. 5. 2 Traffic Situation Description and Analysis as a Key Enabler for ADAS 21 and Homeier, 2010], Situation Graph Trees (SGTs) by [Arens and Nagel, 2003] and OPRMs by [Howard and Stumptner, 2005]. g. a certain behavior. Situation aspects themselves are linguistic variables created by a fuzzy mapping assigning a membership value in addition. Fuzzy rules are executed based on given situation aspects having a high membership value and new aspects are loaded additionally according to the executed fuzzy rules and their consequences.
This is due to its algorithm being mostly dependent on only one selected target object [Häring and Wilhelm, 2009]. In this situation this kind of assistance would be insufficient to avoid a collision. To assess such situations, a more general description considering the relevant objects must be provided. It should also be able to describe relations among these objects and their attributes. This gives a foundation for a situation interpretation that performs reasoning about their behavior or deviations from an expected behavior and the resulting impact as described with Fig.
The method will hence be named minimum class redundancy maximum relevance feature selection (MCRMR). The goal is to further improve the efficient and easy to implement MIFS-based methods. The next section provides an introduction to mutual information followed by a section describing some of the mentioned mutual information based feature selection methods. g. , 2005]). In the discrete case the entropy is calculated with = )ܺ( ܪെ ݔ = ܺ( ) log ଶ ݔ = ܺ( ) , (2) ୀଵ accordingly. For a die roll with 6 evenly distributed die faces the information content or entropy, respectively, is given by (6 ή 1/6 ή logଶ 6) bit ൎ 2,58 bit.