A Stochastic Hybrid Framework For Driver Behavior Modeling Based On Hierarchical Dirichlet Process

Abstract

Scalability is one of the major issues for real-world Vehicle-to-Vehicle network realization. To tackle this challenge, a stochastic hybrid modeling framework based on a non-parametric Bayesian inference method, i.e., hierarchical Dirichlet process (HDP), is investigated in this paper. This framework is able to jointly model driver/vehicle behavior through forecasting the vehicle dynamical time-series. This modeling framework could be merged with the notion of model-based information networking, which is recently proposed in the vehicular literature, to overcome the scalability challenges in dense vehicular networks via broadcasting the behavioral models instead of raw information dissemination. This modeling approach has been applied on several scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data set and the results show a higher performance of this model in comparison with the zero-hold method as the baseline.

Publication Date

7-2-2018

Publication Title

IEEE Vehicular Technology Conference

Volume

2018-August

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/VTCFall.2018.8690570

Socpus ID

85064910079 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85064910079

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