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

Abstract

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.

Research on an Adaptive Method for the Angle Calibration of Roadside LiDAR Point Clouds

“…The commonly used roadside device perception technologies mainly include sensing technologies composed of devices such as Light Detection and Ranging (LiDAR) [3,4], Millimeter Wave Radar (MMW-Radar) [5,6], inductive sensors [7], cameras [8,9], etc. These sensing technologies can be used to monitor and collect real-time data on the movement of vehicles within a certain range on the road surface through either single-sensor perception or multi-sensor fusion perception.…”

“…Such inconsistencies make it challenging to accurately integrate and process data in a multi-sensor fusion perception system, affecting the comprehensive understanding and holistic analysis of traffic situations. The commonly used roadside device perception technologies mainly include sensing technologies composed of devices such as Light Detection and Ranging (LiDAR) [3,4], Millimeter Wave Radar (MMW-Radar) [5,6], inductive sensors [7], cameras [8,9], etc. These sensing technologies can be used to monitor and collect real-time data on the move- The commonly used roadside device perception technologies mainly include sensing technologies composed of devices such as Light Detection and Ranging (LiDAR) [3,4], Millimeter Wave Radar (MMW-Radar) [5,6], inductive sensors [7], cameras [8,9], etc.…”

“…The commonly used roadside device perception technologies mainly include sensing technologies composed of devices such as Light Detection and Ranging (LiDAR) [3,4], Millimeter Wave Radar (MMW-Radar) [5,6], inductive sensors [7], cameras [8,9], etc. These sensing technologies can be used to monitor and collect real-time data on the move- The commonly used roadside device perception technologies mainly include sensing technologies composed of devices such as Light Detection and Ranging (LiDAR) [3,4], Millimeter Wave Radar (MMW-Radar) [5,6], inductive sensors [7], cameras [8,9], etc. The angle calibration of LiDAR point clouds refers to adjusting the collected point cloud data's posture or rotation to align or align them with a specific reference coordinate system.…”