Abstract: 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.