Estimating Sudden Braking using Weather Estimation by a Deep Learning Framework from Drive Recorder Data

Abstract

Data mining based vehicle probe data analysis has addressed many attentions since we have entered the era of big data. In this work, we propose a study of combining probe data with weather information to perform sudden braking estimation using data mining techniques. We train a deep neural network and estimate weather situations from drive recorders in real time. In addition, we also gather information from meteorological observatories to provide further comparisons. Our experimental results show that the usage of weather information have a slight performance improvement over probe data only analysis.