路怒的认知-行为机制及其干预

路怒是导致交通事故的重要原因之一。申请人在中国驾驶员群体上的研究发现驾驶愤怒并不直接导致事故，对愤怒情境的认知方式才会影响驾驶员的行为选择，从而影响事故的卷入率。目前关于愤怒对驾驶安全的影响的研究大多停留于行为层面，缺乏对行为产生的原因和背后的认知加工机制及神经基础的解释以及针对路怒现象的有效干预措施。本项目首先研究驾驶愤怒情绪的产生机理，构建驾驶愤怒研究的标准化范式；其次探讨愤怒情绪和驾驶员认知方式对驾驶过程的影响，从注意、知觉和决策三个方面揭示愤怒影响驾驶行为的认知神经加工机制，建立愤怒与危险驾驶行为关系的情绪-认知-行为模型，为危险驾驶员的精准化识别奠定基础；然后结合外周生理和EEG指标，建构驾驶员路怒状态和行为的多层次识别模型，并创新性地提出基于嗅觉通道的阈下干预方式实现对路怒状态的即时干预，为开发自动化驾驶安全辅助系统提供心理学理论和数据支持。

Road rage is one of the important factors that may results in traffic accident. We have found driving anger is not directly related with traffic accident, the cognitive thoughts behind the wheel is more important to explain the expression of aggressive driving behavior based on Chinese driver samples. Drivers with revenge thoughts express their anger by aggressive behavior more often than drivers with self-instruction coping thoughts, which may lead to higher accidents rate. But most studies in this field are mainly based on behavior questionnaires, has not yet formed a completed theoretical system about the cognitive processing mechanism of road rage. This study aims to explore the effect of anger on the key element and stage of driving behavior and related interference by combined the technique of simulated driving, eye movement and EEG measures. The research contents include: 1) exploring the generation mechanism of driving anger, to construct a standardized a paradigm for the study of driving anger; 2) revealing the effect of anger on typical driving behavior and the cognitive processing phase of driving behavior, to build an emotion-cognition-behavior model of road rage; 3) to recognize the rad rage state during driving based on the results of simulated driving behavior, physiological and EEG index; 4) to validate the effect of subliminal intervention of road rage via the olfactory pathway. These studies will help us to deeply understand the interaction mechanism of emotion and cognition on complex Human machine interaction systems. The results of these studies could provide psychological theory and data support for improving traffic safety and developing real-time driving safety assistant system.