基于模拟实验的低等级公路车辆过弯风险预测模型

Risk Prediction Model of Vehicle Driving in Small Radius Curves Based on Simulation Experiment

通过驾驶模拟实验对低等级公路车辆驾驶数据进行采集,从车辆过弯时的横向风险与纵向风险出发,对弯道的行驶数据进行统计分析,得到8个主要参数,并通过主成分分析筛选出速度标准差、切向加速度标准差及方向盘转速3个核心统计量,再以此为基础进行k均值聚类,分离出弱风险、急刹车、急转弯、强风险4类驾驶风险。最后取车辆过弯的前1/4时间窗口内的3个核心统计参数作为测试集,整条弯道的行驶数据作为训练集,通过Fisher进行建模,该模型的识别精度可达76.5%。

The driving data of car driving in low-grade highway was collected from the simulation experiment.Focus on the horizontal risk versus vertical risk,8 major statistics were calculated from the driving data,from which,3 core statistics(standard deviation of velocity,tangential acceleration and steering wheel speed)were screened by principal component analysis(PCA).Based on the 3 core statistics,4 kinds of risks(weak risk,sharp brake,sharp turn,strong risk)were separated by k-mean clustering.Then,a risk prediction model of vehicle driving in small curves was founded based on Fisher,whose test set was the 3 core statistics of the initial 1/4 period that vehicle driving in curves,and training set was that of the whole curve.The accuracy of the risk prediction model was 76.5%.