Driver Identification through Stochastic Multi-State Car-Following Modeling

• URL: http://arxiv.org/abs/2005.11077v1
• Date: Fri, 22 May 2020 09:39:00 GMT
• Title: Driver Identification through Stochastic Multi-State Car-Following Modeling
• Authors: Donghao Xu, Zhezhang Ding, Chenfeng Tu, Huijing Zhao, Mathieu Moze, Fran\c{c}ois Aioun, and Franck Guillemard
• Abstract summary: Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. 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.
• Score: 7.589491805669563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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