基于模糊认知图的智能汽车驾驶权交互决策建模方法  

Interactive Decision Modeling of Intelligent Vehicle Driving Authority Based on Fuzzy Cognitive Map

作　　者：陈怿淳 靳慧斌[1] 刘希 CHEN Yi-chun;JIN Hui-bin;LIU Xi(Transportation Science and Engineering College,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学交通科学与工程学院,天津300300

出　　处：《科学技术与工程》2024年第25期10964-10973,共10页Science Technology and Engineering

基　　金：国家自然科学基金(62132017)。

摘　　要：针对人机共驾中的由自动驾驶接管到驾驶人接管切换的动态问题,提出一种基于模糊认知图(fuzzy cognitive map,FCM)的驾驶权交互决策模型。首先分别分析影响驾驶风险的车辆和人的动态风险因素,并选取车距、加速度、方向盘转向熵、驾驶员眼动信息、心脑电波信息等相关影响指标构建基于FCM的车辆动态风险模型与人的动态风险模型,再结合环境因素,车辆状况以及驾驶员水平构建基于FCM的驾驶员交互决策模型。为降低对专家知识的依赖,运用基于实数编码的遗传算法(real coded genetic algorithm,RCGA)对模型进行优化,进一步提高模型决策的准确性。仿真结果表明,经RCGA优化后模型适应度值达到0.979,可见该模型能够较好地应对动态、复杂的驾驶环境。A driving authority interaction decision model based on fuzzy cognitive map(FCM)was proposed to address the dynamic transition from autonomous driving to driver takeover in human-machine co-driving scenarios.Firstly,dynamic risk factors influencing driving risks for both the vehicle and the driver were separately analyzed.Relevant influencing indicators such as vehicle spacing,acceleration,steering wheel entropy,driver eye movement information,and electroencephalogram(EEG)signals were selected to construct FCM-based models for dynamic vehicle risk and driver risk.Subsequently,these models were integrated with environmental factors,vehicle conditions,and driver proficiency to establish an FCM-based driver interaction decision-making model.To reduce reliance on expert knowledge,the model was optimized using a real coded genetic algorithm(RCGA)based on real number encoding,thereby enhancing the accuracy of the model s decision-making.The simulation results indicate that the model,after optimization using RCGA,achieves an adaptation fitness value of 0.979.This suggests that the model is capable of effectively addressing dynamic and complex driving environments.

关 键 词：交通运输工程 模糊认知图 驾驶权交互 直觉模糊集 实数编码遗传算法 

分 类 号：U491[交通运输工程—交通运输规划与管理]