Abstract: Provided are a digital twinning method and system for a scene flow based on a dynamic trajectory flow, which belong to the field of traffic control. The method includes: extracting and identifying a target semantic trajectory with a detecting and tracking integrated multi-modal fusion and perception enhancement network; extracting road traffic semantics, so as to obtain a highly parameterized virtual road layout top view; obtaining a road layout traffic semantic grid encoding vector based on the virtual road layout top view; constructing a target coupling relation model; constructing a traffic force constraint model; constructing a long short term memory trajectory prediction network; predicting a motion trajectory of a target with the long short term memory trajectory prediction network, so as to obtain the predicted motion trajectory; and obtaining a digital twin of the scene flow based on trajectory extraction, semantic identification and the predicted motion trajectory.

Abstract: A 3D target detection method based on multimodal fusion and a depth attention mechanism includes the following steps: obtaining and preprocessing original point cloud data and original image data; inputting the preprocessed point cloud data and image data into a 3D target detection network based on multimodal fusion and the depth attention mechanism, where the 3D target detection network based on multimodal fusion and the depth attention mechanism includes a generation phase for 3D bounding box proposals and a refinement phase for 3D bounding boxes, and the network outputs parameters and classification confidence of a target bounding box; training the 3D target detection network based on multimodal fusion and the depth attention mechanism; and processing the collected lidar point cloud data and image data by using the trained detection network, and outputting 3D target information, to realize 3D target detection.

Abstract: A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

Abstract: A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

Abstract: The present invention discloses a target cross-domain detection and understanding method, system and equipment and a storage medium.