We propose a multistep-forward voltage-prediction approach combining a long short-term memory time-distributed model and the Kalman filter algorithm to improve prediction efficiency and reduce the demand for computing...
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We propose a multistep-forward voltage-prediction approach combining a long short-term memory time-distributed model and the Kalman filter algorithm to improve prediction efficiency and reduce the demand for computing capability.
In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object's detection. Firstly, the IAW met...
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In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object's detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors' time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (kf) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion kf algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the kf algorithm, and the Distributed Weighted fusion kf algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.
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