Abstract
Roadside perception systems commonly rely on millimetre-wave radars that are stationary. Obtaining global coordinates of radar detection targets requires calibration of radars installed on roadways, which is dangerous if performed via conventional methods. Hazards such as live traffic, particularly on expressways, make safe calibration difficult without road closures. To avoid these issues, this paper proposes a novel calibration method for roadside millimetre-wave radar by using high-definition maps to provide global positioning for radar detection targets, combined with vehicle trajectory data from live traffic. After clustering collected traffic data, a polynomial curve fit provides the shape of lane centrelines. Calibration parameters for the relative-to-global position mapping are then obtained by matching fitted curve sample points to points in high-definition maps using non-linear optimization. The calibration results are verified experimentally using a connected vehicle and roadside unit on an urban street, demonstrating that the proposed method meets lane-level precision requirements for V2X applications. This data-driven method borrows live traffic for calibration and hence is safer and more convenient compared to existing calibration methods.
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