|Alternative Title||Tnflnence Mechanism and Tnterventi}n Method of Driving Anger|
|Place of Conferral||中国科学院心理研究所|
|Keyword||驾驶愤怒 危险驾驶 情绪识别 音乐干预|
研究三探究驾驶过程中的情绪性状态及危险驾驶行为识别。通过采集并提取研究二中驾驶员在模拟驾驶任务过程中的脑电信号频域特征，采用支持向量机分类器及粒子群寻优算法对愤怒状态的危险驾驶行为进行识别。结果显示:驾驶过程中愤怒情绪的识别正确率为87.26% ， ROC曲线下面积为0. 94;愤怒情绪下危险跟车行为的识别正确率为83.08% ， ROC曲线下面积为0.86 。
Dangerous driving behavior is the direct factor of accidents and seriously threatens traffic safety. Studies had shown that anger (state and trait) could lead to risky driving behavior, which affected the occurrence and severity of traffic accidents. However, the mechanism of anger affecting driving behavior and the intervention methods to eliminate adverse consequences should be explored systematically. From the perspective of cognitive processing, this study systematically throw light on the influence of anger on the specific process of driving behavior, revealed the changes of cognition and performance of driving under the influence of anger, and explored the identification and intervention of anger state on this basis. This paper includes the following four studies:
Study 1 explored the relationship between anger and self-reported driving behavior. Based on the theory of general aggressive behavior, a questionnaire survey was conducted to examine the relationship between the anger trait, driving anger, driver's angry thoughts, and dangerous driving behavior. The results showed that the impact of anger trait on dangerous driving behavior was incompletely mediated by driving anger; driving anger affected dangerous driving behavior by affecting the driver's angry thoughts. It indicated that drivers generated emotions and a certain cognitive evaluation after perceiving the environmental information, which provided basis for the final driving behavior. Furthermore, the whole process was affected by personalities.
Study 2 explored the impact of anger on the driver's capacities and performance in two typical scenarios, car following task (vehicle-to-vehicle interaction) and pedestrian crossing task (vehicle-to-human interaction), using 3 (emotions: angry, happy, neutral) X 2 (trait anger: high, low) mixed experimental design. Experiment 1 required participants to complete the car-following task after recalling the emotional or neutral event. The results showed that both anger and happiness increased drivers' brake response time and the decreased minimum time to collision being compared with the neutral condition. In addition, the driver's perceived accident risk under happy state is significantly lower than the other conditions. Experiment 2 required participants to complete the pedestrian一crossing task after viewing the emotional or neutral film clips.
The results showed that the probability of the driver passing in front of the pedestrian was higher, the speed was faster, the minimum speed when encountering a pedestrian was greater, and the lateral distance from the pedestrian was smaller in anger condition than neutral and happy conditions.
Study 3 explored identification of the angry state and prediction of dangerous driving behavior during driving. By collecting and extracting the frequency domain features of electroencephalogram signal during simulation driving task of Study 2, The results the Support Vector Machine (SVM) classifier and Particle Swarm Optimization (PSO) algorithm showed that the accuracy of anger recognition during driving was 87.26%, and the area under the ROC curve was 0.94; the accuracy of risky car-following behavior recognition was 83.08%, and the area under the ROC curve was 0.86.
Study 4 explored how to reduce the negative impact of anger on driving performance by relaxing music. Both groups were required to complete the simulated driving after the neutral and angry arousal. Under the neutral condition, the two groups did not listen to the music while driving. Under the angry condition, the intervention group listened to the relaxing music while driving, however the control group did not listen to music. The results showed that, in the car-following task, the intervention group had shorter braking response and longer time to collision than the control group; in the pedestrian-crossing task, the intervention group had smaller average driving speed and lateral distance to the pedestrian and perceived lower physical load than the control group.
In summary, this study established a phased and interactive experimental paradigm of anger impact on driving behavior, and systematically revealed the behavior-cognitive processing mechanisms of anger on driving behavior, that is, anger not only impairs driver's response capacity in an emergency, but also leads to more risky behavioral pattern. It can be effectively identified based on the features of EEG activity under rage, and can be alleviated by relaxing music. This study establishes a cognitive-behavior-recognition-intervention research paradigm aiming at driving behavior under rage.Relevant results could deepen the theoretical understanding of the anger influence on driving behavior, and provided theory and thinking for the design of driving safety assistance system.
|CasirLion. 驾驶愤怒的影响机制及干预方法[D]. 中国科学院心理研究所. 中国科学院大学,2019.|
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