  Intra-driver and inter-driver heterogeneity has been confirmed to exist in
human driving behaviors by many studies. In this study, a joint model of the
two types of heterogeneity in car-following behavior is proposed as an approach
of driver profiling and identification. 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 intradriver heterogeneity, while the difference between the
driver profile of different drivers depict the inter-driver heterogeneity.
Thus, the driver profile can be used to distinguish a driver from others. Based
on the assumption, a stochastic car-following model is proposed to take both
intra-driver and inter-driver heterogeneity into consideration, and a method is
proposed 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 are demonstrated and discussed.
