A Trajectory Compensation Method Considering the Car-Following Behavior for Data Missing of Millimeter-Wave Radar in Roadside Detection Applications

Concerning roadside traffic detection applications, and to address the millimeter-wave radar's missing data problem caused by target occlusion or the absence of features in low-speed conditions, this paper proposes a trajectory compensation method regarding car-following behavior. Referring to the installation scheme of the detector, a coordinate transformation method is presented to unify the radar spatial coordinates with the road coordinates. Considering the driver's car-following behavior, the optimal velocity model (OV), full velocity difference model (FVD), and the full velocity difference and acceleration (FVDA) model are applied for tracking the vehicle's trajectory related to the movement of the vehicle ahead. Finally, a data compensation scheme is presented. Taking actual trajectory data as samples, the proposed methods are verifiably useful for compensating for missing data and reconstructing target trajectories. Statistical results of different missing data trajectories demonstrate the rationality of the application of car-following models for the missing data compensation, and the FVDA model performs well compared with the OV and FVD models.

Automated summary

This paper proposes a trajectory compensation method to address the missing data problem of millimeter-wave radar caused by target occlusion or the absence of features in low-speed conditions. A coordinate transformation method is presented to unify the radar spatial coordinates with the road coordinates based on the installation scheme of the detector. Car-following models including the optimal velocity model (OV), full velocity difference model (FVD), and full velocity difference and acceleration (FVDA) model are applied to track the vehicle's trajectory in relation to the movement of the vehicle ahead. The proposed methods are found to be effective for compensating for missing data and reconstructing target trajectories, with the FVDA model performing well compared to the OV and FVD models, as demonstrated by statistical results.