车联网关键指标对车辆安全影响仿真测试方法

A Simulation Scheme for Testing the Effects of Key Indicators of the Internet of Vehicles on Vehicle Safety

提出了一种用于分析车联网关键指标对车辆安全影响的仿真测试方法。首先,基于微观交通流仿真软件设计了危险跟驰、换道等基础仿真场景;然后,分析了基于高斯分布的定位误差模型和单跳通信延误模型,并建立了定位误差、通信延误和渗透率在仿真过程中的执行策略;接着,基于车辆最小安全距离跟驰模型和车辆非线性分段制动模型分别提出了车辆危险跟驰预警和危险换道预警方法;最后,通过建立基于HLA(high level architecture)的车联网仿真平台对不同定位误差、通信延误和渗透率对车辆安全的影响进行了仿真测试。结果表明,在危险跟驰场景中,在注入了基于高斯分布的定位误差后,预警成功率为88%,预警成功率随着预警策略中减速度的减小而增大;在危险换道场景中,在注入了单跳通信延误后,预警成功率达100%;成功预警数随着OD(origin destination)取值和渗透率的增大而增大,并且受渗透率影响更加明显。

A simulation testing scheme for analyzing the effects of the key indicators of the internet of vehicles（IOV） on vehicle safety is proposed.Firstly the basic simulation scenarios of dangerous vehicle following and lane change are devised based on microscopic traffic flow simulation software.Secondly,Gaussian distribution-based vehicle positioning error model and single-hop communication delay model are analyzed,and the executed strategies of positioning error,communication delay and penetration ratio during simulation are determined.Then the ways of warnings for dangerous vehicle following and lane change are proposed based on minimum safety distance model and vehicle nonlinear piecewise braking model respectively.Finally,a high level architecture-based IOV simulation platform is setup and a simulation is conducted for testing the effects of poisoning error,communication delay and penetration ratio on vehicle safety.The results show that in dangerous vehicle following scenario,the success rate of warning increases with the lowering of vehicle deceleration in warning strategy and the bringing-in of Gaussian distribution-based vehicle positioning error can get a warning success rate of 88%,while in dangerous lane change scenario,the success rate of warning increases with the rises of origin-destination value and penetration ratio,in which the latter has more obvious effects,and the bringing-in of single-hop communication delay can achieve a warning success rate of 100%.