Please select the most similar patent number from A, B, C and D. Which number is?
Disclosed is an iterative optimization multi-scale fusion speed prediction algorithm for intelligent connected vehicles, which is characterized in that the following steps are comprised: step 1: establishing multiple regression models for drivers having different driving styles; step 2: assigning a normalized global model weight μ(t) to each regression model in step 1 at a specific moment t to obtain a global fusion predicted vehicle speed; step 3: establishing a local vehicle speed prediction model; step 4: obtaining a global-local fusion predicted vehicle speed by the weighted averages of global and local vehicle speeds within a local time window and by performing a model probability update by means of a probability transfer matrix; step 5: correcting the global-local model weight and the probability transfer matrix in step 4; step 6: calculating to obtain a calibrated global fusion predicted vehicle speed; and step 7: repeating steps 2 to 6. The predicted vehicle speed in the present invention has small error and high accuracy.
A: Provided are data processing methods and an apparatus, a device, and a medium, a method comprising: acquiring local model parameters corresponding to N local recognition models in an rth synchronization period, and performing parameter fusion on the local model parameters corresponding to the N local recognition models, so as to obtain a target global model corresponding to the rth synchronization period; acquiring a historical global model of the N local recognition models in the r-1th synchronization period, and generating, according to the historical global model and the target global model, global federated momentum corresponding to the rth synchronization period; and sending the global federated momentum to N clients, so that the N clients respectively perform parameter update, according to the global federated momentum, on the associated local recognition models. The training efficiency of local recognition models can be improved, and the model performance of an object recognition model obtained on the basis of training the local recognition models can be improved.
B: The present invention relates to a method and system for real-time three-dimensional scan modeling for a large-scale scene, the method comprising the following steps: acquiring an RGB-D video stream; performing global camera posture optimization on the video stream so as to acquire a global camera prediction position; and carrying out globally-optimized three-dimensional scan modeling according to the global camera prediction position. Real-time and terminal-to-terminal modeling is carried out. Thus, local camera tracking is not relied on, but rather the globally-optimized camera positioning is constantly maintained. A parallel optimization modeling system which combines sparse feature points, dense geometry, and color information is put forward and developed by the present invention; a globally-optimized camera position may also be predicted in real time by means of the present invention, camera repositioning is supported, and a globally-consistent optimal three-dimensional model may be updated in real time. The system is applied to large-scale indoor scanning, and a high-quality three-dimensional model may be acquired.
C: Provided are a trajectory prediction method and apparatus, a storage medium, a driving system and a vehicle. The method comprises: acquiring global semantic data and global trajectory data of a region where an object for which prediction is to be carried out is located (S202); then, fusing the global semantic data with the global trajectory data to obtain global fused data (S204); extracting features in the global fused data to obtain global features (S206); and then, processing the global features by means of a trained trajectory prediction model so as to obtain a target trajectory of said object (S208). The method can realize trajectory prediction for a moving object in conjunction with global data, has a relatively high prediction accuracy, and reduces the probability of accidents occurring to some extent.
D: A hybrid vehicle working condition prediction method based on meta-learning. A model training process is divided into two parts: pre-training executed offline and fine-tuning training executed online; the pre-training relates to implementing parallel training for various working conditions to obtain a base model having good generalization performance; the fine-tuning training combines multi-task training on the basis of a deep neural network, and relates to training for a specific working condition on the basis of the base model. The time cost is low, and the method can be applied to a model online correction link. In addition, on the basis of said process, further provided is a vehicle speed prediction model online application framework composed of three parts, i.e., offline training, online training, and real-time prediction, which can be applied to a working condition prediction task under actual traffic conditions.
Answer: D