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

摘要:

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%。

Abstract:

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%.