Research of driver emotion model under simplified traffic condition

被引：6
|
作者：
Xie L. [1 ,2 ]
Wang Z.-L. [1 ,2 ]
Ren D.-C. [3 ]
Teng S.-D. [1 ,2 ]
机构：
[1] School of Information Engineering, University of Science and Technology Beijing
[2] Key Laboratory for Advanced Control of Iron and Steel Process, Ministry of Education, University of Science and Technology Beijing
[3] Institute of Automation, Chinese Academy of Sciences
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2010年 / 36卷 / 12期
关键词：
Driver model; Hidden Markov model (HMM); The self-transition process of emotion; The transition of stimulation on emotion;
D O I：
10.3724/SP.J.1004.2010.01732
中图分类号：
学科分类号：
摘要：
The current driver models in driver assistance systems are usually simple, which do not consider the influence of drivers' emotions on driving strategies. In order to address this issue, in this paper, we study the driver emotion model under simplified traffic conditions. Based on the OCC (Ortony-clore-collins) emotion model, the Markov model of the self-transition process of emotion states, and the hidden Markov model (HMM) of the transition of stimulation on emotion states, we propose two driver emotion models with varying road conditions and with unvarying road conditions, respectively, and we study the varieties of driver emotion during car following, lane switching and overtaking. For the self-transition process, we propose a time-varying self-transition process by taking the real-time varying characteristic of emotion into account; for the transition of stimulation on emotion states, we consider the memory influence of stimulation on emotion, i. e., the same stimulation but with different action time would have different influence on emotion. Meanwhile, we also discuss the influence of varying cognitive emotion on driving strategies. Simulation experiments are conducted on the following aspects: the influences of distance, lane width, and speeds of nearby vehicles on drivers' emotion, the sensitivity to stimulation, and the influence of specific event. The simulations predict what strategy the driver will take under the stimulation of specific event, and the predictions are then verified with real-world data. Experimental results demonstrate the effectiveness of the proposed model. The work proposed in this paper can provide a meaningful theory for building driver models in driver assistance systems. Copyright © 2010 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：1732 / 1743
页数：11
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