Road-range Tracking of Vehicle Trajectories Based on Millimeter-wave Radar

Accurate traffic flow data, particularly vehicle trajectory data with rich spatial-temporal information, have very important research and application value for expressway traffic management and intelligent services. However, owing to the limitation of a single sensor, it is difficult to obtain the road-range level trajectory data in real time using existing vehicle trajectory sensing technology. To this end, a vehicle trajectory perception method based on millimeter-wave radar (MMW) is proposed. The method includes the adaptation of the original MMW radar data, data cleansing and noise reduction, road alignment perception, trajectory matching, and splicing. The adaptation method standardizes the radar data format by constructing an adaptation table. Mirrored trajectory data were eliminated based on the reliability evaluation index K. A road alignment perception method was proposed based on the statistical characteristics of historical trajectories. To overcome the limitations of the perception range, trajectory matching and splicing methods were used to obtain a continuous trajectory within and across devices. Real-time kinematic and video data from an unmanned aerial vehicle were used to test the perception accuracy of the single-vehicle tracking and multiple object tracking of MMW radar systems. The verification results show that in the single-vehicle tracking state, the mean latitude offset was - 0. 284 m, the mean longitude offset was - 0. 352 m, the average error in latitude error was 0. 712 m, and the average error in longitude error was 0. 539 m. In the multiple-object tracking state, the false negative rate of the system was approximately 8%, and the average offset between the track positioning and real position was 0. 990 m, which satisfies the accuracy requirements. This study provides data support for future studies on the risk transfer analysis of individual driving behaviors, spatial-temporal evolution of driving risks at the micro level, impact of traffic accidents, and migration mechanism of traffic congestion in a more macro scope. © 2022 Xi'an Highway University. All rights reserved.