摘要
为准确评价驾驶员风险感知水平,获取不同交通流状态、不同交叉口几何特性、不同干扰情况下驾驶员反应数据、心电生理数据、眼动数据以及行驶过程中车辆性能数据、车辆行为数据、与其他车辆交互情况等,从驾驶员生理、物理指标2个方面分析风险评价指标,在风险感知量化、情景提取和行为分析基础上建立评价指标体系;运用隐马尔可夫模型(HMM)预测驾驶员风险感知量化值,并分析不同风险感知量化值下的驾驶行为参数,得到风险感知量化值的观察序列,进而验证模型有效性。结果表明:用该模型预测驾驶员风险感知量化值能达到85%以上的准确率。
To accurately evaluate the level of driver ’s risk perception during driving,data on driving behavior,electrophysiological status and eye movement of driver ’ s response to different traffic flow conditions,intersection geometry and sudden events were collected,together with the data on vehicle performance,vehicle behavior and the interaction with other vehicles. The risk assessment indicators were analyzed from the perspectives of driver ’ s physiological and physical indicators. Based on the risk perception quantification,scenario extraction and behavior analysis,an evaluation index system was established. Then,by using the HMM,quantitative values of risk perception were predicted,driving behavior parameters under different risk perception quantitative values were analyzed,and an observation sequence of risk perception quantization values was finally obtained. The validity of the model was verified.The results show that the HMM model can predict the quantified value of driver’s risk perception with an accuracy over 85%.
作者
艾倩楠
AI Qiannan(Department of Public Utilities,Jiangsu Urban and Rural Construction College,Changzhou Jiangsu 213147,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2018年第12期144-149,共6页
China Safety Science Journal
