Paper Info
Title
Modeling and adaptation of stochastic driver-behavior model with application to car following.
Abstract
In this paper, we present our recently developed stochastic driver-behavior model based on Gaussian mixture model (GMM) framework. The proposed driver-behavior modeling is employed to anticipate car-following behavior in terms of pedal control operations in response to the observable driving signals, such as the own vehicle velocity and the following distance to the leading vehicle. In addition, the proposed driver modeling allows adaptation scheme to enhance the model capability to better represent particular driving characteristics of interest (i.e., individual driving style) from the observed driving data themselves. Validation and comparison of the proposed driver-behavior models on realistic car-following data of several drivers showed the promising results. Furthermore, the adapted driver models showed consistent improvement over the unadapted driver models in both short-term and long-term predictions.
Year
DOI
Venue
2011
10.1109/IVS.2011.5940464
Intelligent Vehicles Symposium
Keywords
Field
DocType
Gaussian processes,road vehicles,Gaussian mixture model,car-following behavior,driver-behavior modeling,driver-behavior models,observable driving signals,pedal control operations,realistic car-following data,stochastic driver-behavior model,unadapted driver models
Car following,Data modeling,Observable,Control engineering,Gaussian process,Engineering,Hidden Markov model,Mixture model
Conference
ISSN
Citations 
PageRank 
1931-0587
7
0.58
References 
Authors
0
3
Name
Order
Citations
PageRank
Pongtep Angkititrakul117915.47
Chiyomi Miyajima234545.71
Kazuya Takeda31301195.60