driver2vec_Reproduced

A reproduction of the paper Driver2vec: Driver Identification from Automotive Data. This paper aims to identify drivers through their driving habits, making use of a triplet loss function when comparing the drivers within a set. This model also makes use of a Temporal Convolutional Network (TCN) to output a driver's driving "fingerprint" that can be used to identify a driver within a set of drivers with varying degrees of accuracy depending on the number of drivers in a given set. After the TCN, the model then makes use of LightGBM, a gradient boosting method that is used to ultimately predict the correct driver from a small sample of the total driving time within a set.