Roadside intelligent sensors can monitor road traffic status, and the installation method of sensing equipment greatly affects the richness and accuracy of traffic data information collection. The radar-video integrated machine integrates radar and camera, and is a new type of perception device. This paper conducted experimental analysis on the installation position, rotation angle, and other aspects of the radar-video integrated machine. Based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), an optimal solution was proposed. Firstly, before designing the installation plan, analyzed the main factors affecting the installation plan, which were used as a single variable to design 5 sets of operating conditions. Next, data collection and analysis were conducted based on the built the Radar-Video integrated machine platform, and 9 indicator parameters were selected to construct an evaluation system. Finally, comprehensive evaluation and optimization were conducted on each installation plan. This paper selected two on-site scenarios for experimental verification, and the results show that the proposed evaluation technology can successfully optimize the installation plan. However, according to different installation scenarios, such as lane width and installation height, the optimal installation plan may be adjusted.
Most of the current trajectory tracking algorithms, which only rely on a single data source sensor for target trajectory tracking, are prone to problems such as inaccurate acquisition of target velocity information and unstable tracking. In this paper, based on the existing 2D and 3D trackers, a trajectory information fusion algorithm based on adaptive weight coefficients is proposed based on the consideration of the distance decay characteristics of point cloud quality (quantity, density, etc.), which effectively improves the accuracy and robustness of the trajectory tracking method, expands the trajectory tracking range, alleviates the target loss phenomenon, realizes the mutual fusion of multi-dimensional trajectory information under the same time and space, and makes up for the shortcomings of a single sensor when tracking the target trajectory.
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