Analysis and Verification of the Perception Accuracy of Millimeter-wave Radars on Smart Roads
Millimeter-wave(MMW)radars are important components of roadside perception systems on smart roads and have been widely used to monitor the status of traffic flow operation,for intelligent control,vehicle-road collaboration,and autonomous driving.However,changes in the relative position and posture of vehicles in traffic and the MMW radar may affect the radar signal echo and point cloud distribution,leading to deviations in the radar perception results of vehicle positions.It is crucial to analyze the spatial distribution characteristics of the perception accuracy of MMW radars to guide their application on smart roads.Based on the perception principle of the MMW radar,this study comprehensively considers the sources of perception errors in the two stages of MMW radar signal processing and point cloud data processing.Through a combination of numerical simulations and field experiments,qualitative and quantitative analyses were conducted on the perception accuracy characteristics of MMW radar under different relative vehicle positions and postures.The results show that the longitudinal perception accuracy of radar is mainly affected by the relative position of the vehicle.When the vehicle and radar are too close(longitudinal distance less than 30 m)or too far(longitudinal distance more than 200 m),the perception result can significantly shift towards the front or rear of the vehicle,and the longitudinal perception error usually exceeds 0.5 m.The lateral perception accuracy of radar is mainly affected by the lateral position and relative posture of the vehicle.When the vehicle deviates too much from the radar center beam(more than 5 m)or the yaw angle is large(more than 40°),the perception result can significantly shift towards the side of the vehicle body and the lateral perception error usually exceeds 0.5 m.Based on the analysis of the influencing factors,this study further provides guidance for the application of MMW radar perception data and the deployment of perception devices on smart roads.
NETL
NSTL
万方数据
维普
