Abstract
Techniques to enable automated driving currently receive a lot of attention in computer science research. Car automation requires realizing several cognitive functions by computers. One important functionality is environment perception. This consists of several sub-tasks which are complex and thus computation intensive when implemented. We propose the use of decision graphs to speed up the execution of a consistency check. This check is applied to the output of a of neural net which classifies regions in an environment image. The check consists in evaluating a set of probabilistic rules. The paper describes how the miAamics approach of pre-computing results of rule evaluations with decision graphs may be profitably used in this application.
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Hungar, H. (2019). Applying Decision Graphs in the Context of Automated Driving. In: Margaria, T., Graf, S., Larsen, K. (eds) Models, Mindsets, Meta: The What, the How, and the Why Not?. Lecture Notes in Computer Science(), vol 11200. Springer, Cham. https://doi.org/10.1007/978-3-030-22348-9_2
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DOI: https://doi.org/10.1007/978-3-030-22348-9_2
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