Driver Identification through Stochastic Multi-State Car-Following Modeling

TL;DR

This paper introduces a stochastic multi-state model for driver identification that captures both intra-driver and inter-driver heterogeneity, achieving over 82% accuracy in naturalistic driving data.

Contribution

It proposes a novel joint modeling approach for driver heterogeneity and a method for learning driver profiles from car-following data.

Findings

Achieved 82.3% accuracy in driver identification.

Demonstrated fast registration of new drivers.

Validated on naturalistic car-following data.

Abstract

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.