Traffic and pedestrian risk inference using harmonic systems

Abstract

Vehicle and pedestrian risks can be modeled in order to advise drivers and persons. A good model requires the ability to adapt itself to several environmental variations and to preserve essential information about the area under scope. This paper aims to present a proposal based on a Machine Learning extension for timing named Harmonic Systems. A global description of the problem, its relevance, and status of the field is also included.