A method of group vehicle recognition and tracking based on roadside millimetre-wave radar was proposed to improve roadway traffic detection accuracy.Based on pre-processed detection data of millimetre-wave radar on multi-lane traffic flow in an urban arterial road,a Gaussian kernel-distance based spatial clustering algorithm with noise density(DBSCAN)was proposed to conduct spatio-temporal clustering of effective radar signals reflected by group vehicles.Then,a fusion algorithm of unscented Kalman filter(UKF)and linear Gaussian mixture probability hypothesis density(GMPHD)was proposed to improve tracking accuracy of group vehicles which move nonlinearly on the road.The algorithms were tested in simulation and onsite environment.Simulation results verified that the UK-GMPHD algorithm can accurately and stably track nonlinear moving vehicles.Results of onsite test showed that the kernel-distance based DBSCAN algorithm can solve the problem of classical algorithm effectively that the parameter tuning of feature vector was difficult to adjust.The UK-GMPHD algorithm reduced the root mean square error of target tracking in term of target distance,velocity and angle by 21.03%,23.41%and 20.67%in comparison with GMPHD algorithm.
关键词
智能交通/毫米波雷达/群体车辆/目标识别/车辆跟踪/滤波/高斯混合概率假设密度
Key words
intelligent transportation/millimeter wave radar/group vehicles/target recognition/vehicle tracking/filter/Gaussian mixture probability hypothesis density
维普
万方数据