IV'11 Paper Abstract


Paper WePoster1T1.3

Angkititrakul, Pongtep (Nagoya University), Miyajima, Chiyomi (Nagoya University), Takeda, Kazuya (Nagoya University)

Modeling and Adaptation of Stochastic Driver-Behavior Model with Application to Car Following

Scheduled for presentation during the Poster Session "Poster 1 on Wednesday" (WePoster1T1), Wednesday, June 8, 2011, 09:30−11:00, Room T1

2011 Intelligent Vehicles Symposium, June 5-9, 2011, Baden-Baden, Germany

This information is tentative and subject to change. Compiled on December 13, 2018

Keywords Vehicle Control, Decision and Expert Systems, Active and Passive Safety


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.



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