| Summary: | Abstract Vehicle-to-infrastructure (V2I) is one of the effective ways to solve the problem of intelligent connected vehicle perception, and the core is to fuse the information sensed by vehicle sensors with that sensed by infrastructure sensors. However, accurately matching the objects detected by the vehicle with multiple objects detected by the infrastructure remains a challenge. This paper presents an object association matching method to fuse the object information from vehicle sensors and roadside sensors, enabling the matching and fusion of multiple target information. The proposed object association matching algorithm consists of three steps. First, the deployment method for vehicle sensors and roadside sensors is designed. Then, the laser point cloud data from the roadside sensors are processed using the DBSCAN algorithm to extract the object information on the road. Finally, an improved single-pass algorithm for object association matching is proposed to achieve the matched target by setting a change threshold for selection. To validate the effectiveness and feasibility of the proposed method, real-vehicle experiments are conducted. Furthermore, the improved single-pass algorithm is compared with the classical Hungarian algorithm, Kuhn–Munkres (KM) algorithm, and nearest neighbor (NN) algorithm. The experimental results demonstrate that the improved single-pass algorithm achieves a target trajectory matching accuracy of 0.937, which is 6.60%, 1.85%, and 2.07% higher than the above-mentioned algorithms, respectively. In addition, this paper investigates the curvature of the target vehicle trajectory data after fusing vehicle sensing information and roadside sensing information. The curvature mean, curvature variance, and curvature standard deviation are analyzed. The experimental results illustrate that the fused target information is more accurate and effective. The method proposed in this study contributes to the advancement of the theoretical system of V2I cooperative perception and provides theoretical support for the development of intelligent connected vehicles.
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