Driver Identification Through Heterogeneity Modeling in Car-Following Sequences

Abstract

Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. This research proposes a driver identification method by modeling such heterogeneities in car following sequences. It is assumed that all drivers share a pool of driver states; under each state, a car-following data sequence obeys a specific probability distribution in feature space; each driver has his/her own probability distribution over the states, called driver profile, which characterize the intra-driver heterogeneity, while the difference between the driver profile of different drivers depicts the inter-driver heterogeneity. Thus, the driver profile can be used to distinguish a driver from others. Based on the assumption, a method of driver identification is proposed to take both intra- and inter-driver heterogeneity into consideration, and a method is developed to jointly learn parameters in behavioral feature extractor, driver states, and driver profiles. Experiments demonstrate the performance of the proposed method in driver identification on naturalistic car-following data: accuracy of 82.3% is achieved in an 8-driver experiment using 10 car-following sequences of duration 15 seconds for online inference. The potential of fast registration of new drivers is demonstrated and discussed.