A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process
复制标题
基于分层狄利克雷过程的驾驶员行为建模随机混合框架
DOI：
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发表时间：
2017
期刊：
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通讯作者：
Y. P. Fallah
中科院分区：
文献类型：
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作者：
Hossein Nourkhiz Mahjoub;Behrad Toghi;Y. P. Fallah
可扩展性是实现车对车网络的主要问题之一。为了解决这一问题，本文研究了一种基于非参数贝叶斯推理方法的随机混合建模框架，即层次Dirichlet过程（HDP）。该框架能够通过对车辆动态时间序列的预测，对驾驶员/车辆行为进行联合建模。该建模框架可以与最近在车辆文献中提出的基于模型的信息网络概念相结合，通过广播行为模型而不是原始信息传播来克服密集车辆网络中的可扩展性挑战。该建模方法已应用于实际安全驾驶模型部署（SPMD）驾驶数据集的多个场景，结果表明该模型的性能优于以零保持方法为基准的模型。
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.