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作 者:郭凤香[1] 马传戬 蔡晶 周怡雯 李京阳 GUO Fengxiang;MA Chuanjian;CAI Jing;ZHOU Yiwen;LI Jingyang(School of Transportation Engineering,Kunming University of Technology,Kunming Yunnan 650500,China)
机构地区:[1]昆明理工大学交通工程学院,云南昆明650500
出 处:《中国安全生产科学技术》2024年第9期219-226,共8页Journal of Safety Science and Technology
基 金:国家自然科学基金项目(71961012);云南省教育厅科学研究基金项目(2024Y132);昆明理工大学分析测试基金项目(2022M20202106029)。
摘 要:为研究冲突情景下老年驾驶人的焦虑水平,利用量表量化老年驾驶人焦虑程度,通过搭建冲突交叉口道路虚拟场景,采集不同冲突情境下老年驾驶人的驾驶行为数据。运用Spearman相关性分析法,筛选出与老年驾驶人焦虑水平相关的影响因子,基于径向基函数(RBF)神经网络和BP神经网络分别建立老年驾驶人焦虑水平预测模型,并对比2种模型的预测性能。研究结果表明:不同冲突情境下,老年驾驶人的年龄、驾龄、制动踏板深度、方向盘转角熵、冲突严重度等级与焦虑水平成显著正相关关系,速度与焦虑水平呈显著负相关关系;基于RBF神经网络的老年驾驶人焦虑模型的预测准确率为87.14%,精确率为88.24%,召回率为68.18%,F_(1)值为76.92%。基于BP神经网络的老年驾驶人焦虑模型的预测准确率为92.86%,精确率为90.48%,召回率为83.36%,F_(1)值为88.37%。2种模型均能够较好地预测老年驾驶人的焦虑水平,且基于BP神经网络的老年驾驶人焦虑预测模型预测性能更优。研究结果可为正确识别老年驾驶人的焦虑水平提供一定的理论基础,对于创造安全高效的驾驶具有重要意义。To investigate the anxiety levels of elderly drivers in the conflict scenarios,a scale was employed to quantify the anxiety degree of elderly drivers.A virtual scenario of conflict intersection road was constructed to collect the driving behavior data of elderly drivers in different various conflict situations.The Spearman correlation analysis method was utilized to screen out the factors influencing the anxiety levels of elderly drivers.The prediction models for the anxiety levels of elderly drivers were established by using radial basis function(RBF)neural network and backpropagation(BP)neural network respectively,and the prediction performance of the two models was compared.The results show that in different conflict situations,the age,driving years,brake pedal depth,steering wheel angle entropy,and conflict severity present the significant positive correlation with the anxiety level,while the speed presents a significant negative correlation with the anxiety level.The prediction accuracy of the elderly driver anxiety model based on RBF neural network is 87.14%,the accuracy rate is 88.24%,the recall rate is 68.18%,and the F_(1) value is 76.92%.The prediction accuracy of the elderly driver anxiety model based on BP neural network is 92.86%,the accuracy rate is 90.48%,the recall rate is 83.36%,and the F_(1) value is 88.37%.Both models can better predict the anxiety level of elderly drivers,and the anxiety prediction model of elderly drivers based on BP neural network has better prediction performance.The research results can provide a theoretical basis for correctly identifying the anxiety level of elderly drivers,and are of great significance for creating the safe and efficient driving.
关 键 词:冲突情景 老年驾驶人 焦虑水平 RBF神经网络 BP神经网络
分 类 号:X951[环境科学与工程—安全科学] U492.8[交通运输工程—交通运输规划与管理]
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