Abstract: A method and apparatus for achieving vehicle-road coordination at an intersection without signal lights, the method comprising: acquiring information used for trajectory planning, and inputting the information used for trajectory planning into a model for planning a vehicle driving trajectory to obtain planned trajectory information, wherein the model used for planning the vehicle driving trajectory determines constraint conditions for avoiding collisions on the basis of road region width information in a plane coordinate system, the coordinates of obstacles, and the coordinates and traffic action types of each vehicle, and determines a loss function on the basis of motion state parameters of each vehicle and the coordinates of each vehicle; and on the basis of the planned trajectory information, driving each vehicle to drive.

Abstract: A data processing method for acoustic event includes: establishing a simulated acoustic frequency event module, a data capturing module, and a sound application decision module in a software manner, setting a simulated hardware parameter to the simulated acoustic frequency event module, inputting a sound signal to a frequency filtering module of the simulated acoustic frequency event module, and obtaining metadata from a frequency event quantizer of the simulated acoustic frequency event module, dividing each of the metadata into multiple frames according to a time interval by the data capturing module, accumulating an event number of each frame by the data capturing module, setting a label of each frame according to the event number, storing these frames, the event number and the label in a database, and training a decision model by the sound application decision module according to the database and a sound application.

Abstract: A method and apparatus for achieving vehicle-road coordination at an intersection without signal lights, the method comprising: acquiring information used for trajectory planning, and inputting the information used for trajectory planning into a model for planning a vehicle driving trajectory to obtain planned trajectory information, wherein the model used for planning the vehicle driving trajectory determines constraint conditions for avoiding collisions on the basis of road region width information in a plane coordinate system, the coordinates of obstacles, and the coordinates and traffic action types of each vehicle, and determines a loss function on the basis of motion state parameters of each vehicle and the coordinates of each vehicle; and on the basis of the planned trajectory information, driving each vehicle to drive.

Abstract: Provided is a multi-vehicle collaborative trajectory planning method, apparatus (600) and system, and a device, a storage medium, and a computer program product. The method comprises: determining a specific number of different multi-vehicle priority schemes for multiple vehicles (S101); determining, by using a sequential planning policy, a corresponding collaborative planning scheme for each multi-vehicle priority scheme (S102); performing quality evaluation on each collaborative planning scheme to obtain a quality evaluation result (S103); and according to the quality evaluation result, determining a target collaborative planning scheme from the specific number of collaborative planning schemes (S104).

Abstract: The present disclosure discloses a voice conversion training method. The method includes: forming a first training data set including a plurality of training voice data groups; selecting two of the training voice data groups from the first training data set to input into a voice conversion neural network for training; forming a second training data set including the first training data set and a first source speaker voice data group; inputting one of the training voice data groups selected from the first training data set and the first source speaker voice data group into the network for training; forming the third training data set including the second source speaker voice data group and the personalized voice data group that are parallel corpus with respect to each other; and inputting the second source speaker voice data group and the personalized voice data group into the network for training.

Abstract: The present disclosure discloses a voice conversion training method. The method includes: forming a first training data set including a plurality of training voice data groups; selecting two of the training voice data groups from the first training data set to input into a voice conversion neural network for training; forming a second training data set including the first training data set and a first source speaker voice data group; inputting one of the training voice data groups selected from the first training data set and the first source speaker voice data group into the network for training; forming the third training data set including the second source speaker voice data group and the personalized voice data group that are parallel corpus with respect to each other; and inputting the second source speaker voice data group and the personalized voice data group into the network for training.

Abstract: An assembly method of a voice coil motor for assembling component parts of a voice coil motor includes the following steps: (1) providing identifiable glue, and the identifiable glue is made by mixing colorable substance or phosphor into transparent glue; (2) coating the identifiable glue on connecting positions of the component parts; (3) curing the identifiable glue, and identifying the identifiable glue simultaneously to check dispensing and curing effect of the component parts. The assembly method can cure and check the voice coil motor simultaneously, thereby it can improve the production efficiency, reduce the manpower, and promote the development of the automated production line. An assembly device is also disclosed.