摘要
为了提升车辆的安全性和能量利用率,从路径规划的层面出发,针对避免车辆遇到极端工况及低效率工况的问题,提出将车辆稳定性判据模型和交通流模型相结合的方法来规划车辆路径,使得车辆在路面湿滑情况下实现快速、安全的行驶。使用交通流模型预测车辆未来将要面临的交通环境变化,再使用稳定性判据模型评估未来交通的安全性,以便为混合动力车辆规划出最快且最安全的路径。具体来讲,为了预测混合动力车辆未来将要面临的车速及车流密度的变化,使用通量矢量分裂格式求解广义Aw-Rascle-Zhang(GARZ)宏观交通流模型。此外,使用驾驶人在环仿真平台PreScan,收集了同一驾驶人在不同车速及不同相对前车距离时给出的前轮转向角响应。基于前轮驱动(FWD)前轮转向(FWS)车辆和全轮转向(AWS)分布式驱动车辆(DDV)的Simulink模型,给出了不同前轮转向角对应的轮胎力饱和因子(δTFSC)响应。使用人工神经网络训练不同车速和车流密度对应的δTFSC,建立了车辆的稳定性判据模型。使用新建立的稳定性判据模型对交通流模型预测的参数(车流速及车流密度)进行稳定性评估。然后,基于以上的方法优化了车辆行驶路径,以确保车辆在湿滑路面上的行驶安全。最后,使用US-101真实交通流数据来验证交通流模型的预测结果。经实例验证得出:交通流模型与车辆横向稳定性判据模型相结合可以从路径规划的层面保证车辆安全行驶并提升交通系统的通行效率。
A method combining vehicle stability criterion and traffic flow models is proposed to plan the vehicle path and avoid the problem of extreme and inefficient conditions affecting the vehicle’s safety and efficiency improvement(from the perspective of path planning).This would ensure the vehicle could be driven quickly and safely under wet road surface conditions.The aim of this study was to plan the fastest and safest route for hybrid electric vehicles.A traffic flow model was used to predict changes in the traffic environment and assess the conditions the vehicle would face in the future,and the stability criterion model was used to evaluate the safety of future traffic.Specifically,to predict the changes in speed and traffic density that hybrid vehicles could face in the future,the generalized Aw-Rascle-Zhang(GARZ)macroscopic traffic flow model was solved using the flux vector splitting format.Additionally,PreScan,a driver-in-theloop simulation platform,was used to collect the front wheel steering angle responses given by the same driver at different speeds and different relative distances to the vehicle in front.Based on the Simulink model of front-wheel drive(FWD)front-wheel steer vehicle(FWS),and distributed drive(DDV)all-wheel steer vehicle(AWS),this study presents the response of tire force saturation coefficient(δTFSC)corresponding to different front-wheel steering angles.The artificial neural network was used to train theδTFSCcorresponding to different speed and traffic density,and the vehicle stability criterion model was established.The new stability criterion model was used to evaluate the stability of the parameters(traffic velocity and traffic density)predicted by the traffic flow model.Based on the above method,the vehicle driving path was optimized to ensure driving safety on a slippery road.US-101 real traffic flow data was used to verify the predicted results of the traffic flow model.An example shows that the combination of the traffic flow model and the vehicle lateral stability criterion model can ensure safe driving of the vehicle and improve the traffic efficiency of the traffic system from the path planning level.
作者
李麟
裴玉龙
尹亮
周亮
LI Lin;PEI Yu-long;YIN Liang;ZHOU Liang(School of Traffic and Transportation,Northeast Forest University,Harbin 150040,Heilongjiang,China;The 32381 Unit of Chinese PLA,Beijing 100072,China)
出处
《中国公路学报》
EI
CAS
CSCD
北大核心
2020年第8期71-80,共10页
China Journal of Highway and Transport
基金
国家自然科学基金重点项目(51638004)。
关键词
交通工程
路径规划
机器学习
GARZ交通流模型
车辆操纵稳定性判据
traffic engineering
path planning
machine learning
GARZ traffic flow model
vehicle handling stability criterion
作者简介
李麟(1988-),男,内蒙古包头人,副教授,工学博士,E-mail:lilin@nefu.edu.cn;通讯作者:裴玉龙(1961-),男,黑龙江桦川人,教授,博士研究生导师,工学博士,E-mail:peiyulong@nefu.edu.cn。
