A novel method for calibration and verification of roadside millimetre‐wave radar

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.

Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data

“…MMW radars, which ofer better tracking capability and speed accuracy compared to video cameras [14], have gained increasing popularity for real-time trafc data collection [3]; for these sensors, as geometric information cannot be directly recorded, studies have attempted to estimate geometric information from extracted trajectories. For instance, roadside MMW radar can extract road information through trajectory clustering and ftting [15].…”

“…Tese methods may also not be applicable in all scenarios, such as at night or in low visibility, and often require clear lane lines for accurate results [16,17]. Indirect methods, which rely on the accuracy of trajectory data [18], can provide some level of accuracy [15], but the range of geometric information obtained is usually limited [15,18] and may require supplementary information for completeness [19]. Additionally, sensor fusion technology, which combines multiple sensors to improve accuracy, can be expensive due to the need for periodic recalibration and can be afected by ofsets caused by wind and vibration.…”