Hybrid enhanced intelligent trajectory prediction method and device based on hybrid grey wolf optimization SVM
A trajectory prediction and intelligent technology, which is applied in the directions of measuring devices, image enhancement, surveying and navigation, etc., can solve problems such as the inability to guarantee normal and safe production, and achieve the effect of real, convenient and effective judgment results, high practicability, and accurate and efficient prediction results
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Embodiment 1
[0057] reference figure 1 As shown, this embodiment discloses a hybrid enhanced intelligent trajectory prediction method based on a hybrid gray wolf optimized SVM, which is applied to a coke pushing vehicle in a coke oven area. The method includes the following steps.
[0058] Step S100: Use lidar to obtain real-time position information of the moving target including pedestrians and vehicles in front of the push-focus vehicle.
[0059] The operation target in this step can be roughly divided into five types of moving objects, including random pedestrians, bicycles, battery cars, electric tricycles, and large, medium and small four-wheeled vehicles. Of course, the goal of sports is not limited to this.
[0060] In this embodiment, the motion trajectories of pedestrians, bicycles, battery cars, electric tricycles, and large, medium and small cars are random, real, and real-time, instead of moving at a moving speed and rest assured.
[0061] In an embodiment, in step S100, obtaining rea...
Embodiment 2
[0103] reference Picture 12 This embodiment provides a hybrid enhanced intelligent trajectory prediction device based on a hybrid gray wolf optimized SVM, which uses the hybrid enhanced intelligent trajectory prediction method based on a hybrid gray wolf optimized SVM of the embodiment.
[0104] The device is applied to a coke pushing car in the coke oven area, and the device includes a positioning module 100, a classification module 200, and a prediction module 300.
[0105] The positioning module 100 is configured to use lidar to obtain real-time position information of moving targets including pedestrians and vehicles in front of the push-focus vehicle.
[0106] The classification module 200 is configured to classify each moving target in the coke oven area based on the human-in-the-loop hybrid enhanced intelligence concept.
[0107] The prediction module 300 is configured to embed the position information and classification results of each operation target into the support vector ...
Embodiment 3
[0109] This embodiment provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the hybrid gray wolf-based Optimize the steps of SVM's hybrid enhanced intelligent trajectory prediction. This step includes:
[0110] Step S100: Use lidar to obtain real-time position information of the moving target including pedestrians and vehicles in front of the push-focus vehicle. Step S200: Based on the human-in-the-loop hybrid enhanced intelligence concept, each moving target in the coke oven area is classified. Step S300: Embed the position information and classification results of the running target into the support vector machine SVM kernel function with classification prediction function, and combine the Levi flight, gray wolf algorithm and the adaptively improved differential evolution algorithm to optimize the SVM kernel function parameters and Penalty factor, th...
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