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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.
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.
In the field of oral implantology, our goals were to investigate the diagnostic utility of traditional CT imaging technology and 3D CBCT imaging technology based on the Internet of Things (IoT) dental Computed Tomography assisted CBCT, as well as to compare and contrast their respective advantages. 72 patients were split evenly between two groups: a control group with 35 participants and a study group with 37 participants. (1) Surgical indexes: The operation time of the control group was (27.96+4.64) minutes and (14.08+3.14) minutes, and the intraoperative blood loss was (16.36+2.95) ml and (5.85+0.64) ml, respectively. The time it took to do the procedure in the experimental group was (14.08+3.14) minutes. In the group that was under examination, both the total amount of time spent operating and the amount of blood lost during the procedure were significantly lower (P <0.05). (2) Dental function: There were no statistically significant variations in dental beauty, comfort, chewing, or retention between the two groups before surgery (P >0.05). This was determined by comparing the results of the dental examinations. After surgery, the dental function of the research group was significantly greater than that of the control group, and the difference between the two groups was statistically significant (P <0.05). (3) Theraputic effect: the effective rate of the treatment in the control group was 85.71 percent, while the effective rate in the study group was 94.59%. (4) Quality of life: when compared with the control group, the comprehensive quality of life of physiological, social, emotional, and cognitive functions in the research group was higher, and the difference was statistically significant (P < 0.05); (5) Satisfaction: the control group’s level of satisfaction was 80.00%, while the study group’s level of satisfaction was 94.59%. (6) Cost: the cost of the treatment in the research group was significantly lower than the cost of the treatment in the control group. As a result, the dental computed tomography assisted cone beam computed tomography (CBCT) 3D imaging technology based on the internet of things should be considered for implementation.
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