Detecting Near Miss Incidents in Bicycle Traffic Using Acceleration Sensor Data

Abstract

Nowadays, with the improvement of the hardware devices and its processing capabil- ities as well as the big data field, a new promising stage in the Artificial Intelligence is born. Hence, a lot of useful applications involving AI are being developed which enable automatizing activities, patterns and voice recognition, pattern and image classification. . . Taking all this into consideration, this thesis intended to develop an incident detector and classifier using data collected by the SimRa App. This App records the rides from cyclists using the accelerometer sensor of the smartphone where it runs. First, the dataset will be overviewed to get an insight of how many useful data is available. Then a data extraction and a subsequent signal enhancement, with filtering and other signal processing techniques like ICA, will be performed in order to improve the quality of the signal that will feed the end classifier. Afterwards, a data adaptation and the developing of several classifiers will be done in order to obtain a reliable incident detector and a satisfactory incident classifier. Finally, an evaluation of the full algorithm processing time will be held as a means to know the viability of the deployment in the SimRa smartphone App.