Large-scale Driver Identification Using Automobile Driving Data

We address a large-scale driver identification problem, which aims to predict the driver of a vehicle from various types of data, such as speed and acceleration information, that are collected during driving by using GPS sensors equipped with smart phones. While existing studies consider at most a few hundreds of drivers, we target a huge number of drivers up to 10,000 drivers. The results of our experiments show that our method identifies drivers more precisely than baseline methods. We also show that location features are quite effective in the large scale driver identification, and speed and acceleration features also contribute to driver identification.