As a typical application of edge intelligence, 3D object detection in autonomous driving often requires multimodal information fusion to accurately perceive the environment. With images and point clouds serving as critical sensory data sources, 3D object detection integrates multimodal fusion to enhance detection accuracy. Generally, fusion algorithms leveraging attention mechanism can intelligently extract and integrate multimodal sensing information to overcome limitations posed by sensor calibration. However, attention mechanism may cause challenges such as slow model convergence and high false positives. Therefore, in this paper, we propose the Deformable Denoising (DefDeN) model to effectively integrate modules including gated information fusion networks, multi-scale deformable attention mechanisms, noise addition and denoising method, and contrastive learning for multi-sensor feature fusion. Experimental results on the nuScenes dataset demonstrate the superiority of DefDeN in detection accuracy, and the effectiveness of precise and stable perception for complex scenarios in autonomous driving systems.

In recent years, in order to achieve the goal of “carbon peaking and carbon neutralization”, many countries have focused on the development of clean energy, and the prediction of photovoltaic power generation has become a hot research topic. However, many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation, and they rarely consider the multi-features fusion methods for power prediction. This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China, and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation. Next, the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets. Finally, traditional time series prediction methods, such as Recurrent Neural Network (RNN), Convolution Neural Network (CNN) combined with eXtreme Gradient Boosting (XGBoost), are applied to study the impact of different feature fusion methods on power prediction. The results show that the prediction accuracy of Long Short-Term Memory (LSTM), stacked Long Short-Term Memory (stacked LSTM), Bi-directional LSTM (Bi-LSTM), Temporal Convolutional Network (TCN), and XGBoost algorithms can be greatly improved by the method of integrating historical generation power and meteorological features. Therefore, the feature fusion based photovoltaic power prediction method proposed in this paper is of great significance to the development of the photovoltaic power generation industry.