Abstract: A fast gain-dependent mix DC offset calibration circuit includes: a DC offset calibration controller, a mix DC offset calibrator, and a digital-domain DC offset calibrator. The DC offset calibration controller is configured to output a first control signal to start the mix DC offset calibrator based on state information sent by a state controller of the zero-IF receiver. The mix DC offset calibrator is configured to execute a mix DC offset calibration algorithm to perform mix DC offset calibration and obtain a calibration codeword when the first control signal is at a first value. The digital-domain DC offset calibrator is configured to execute a digital-domain DC offset calibration algorithm when the first control signal is at a second value, and perform digital-domain DC calibration based on the calibration codeword.

Abstract: The present disclosure provides a game scene generating method, including: separately obtaining pathfinding graphs of a plurality of sub-virtual objects; determining a plurality of reference lines of the pathfinding graph of each sub-virtual object; determining at least one target geometric area in the pathfinding graph on the basis of the plurality of reference lines; and splicing the plurality of sub-virtual objects according to the at least one target geometric area corresponding to each sub-virtual object to obtain a game scene. A virtual game character performs pathfinding on the terrain of the game scene.

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: 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 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.